advanced_subtype string | answer_policy string | difficulty string | difficulty_detail string | difficulty_name string | domain string | evaluation_scope string | execution dict | input_order string | output_schema list | protected_fields_omitted list | question string | redistribution_status string | release_id string | runtime_mode string | source_benchmark string | source_database string | source_database_aliases list | source_license_summary string | source_split string | source_task_id string | source_url string | sources list | streaming_instruction string | task_id string | task_origin string | taxonomy dict | taxonomy_scheme string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "payment_method_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many different payment methods are there? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_deliveries | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2845 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(10)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the number of different payment methods. | spider1_0052 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "customer_name",
"position": 1
},
{
"name": "customer_phone",
"position": 2
},
{
"name": "customer_email",
"position": 3
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | List the names, phone numbers, and emails of all customers sorted by their dates of becoming customers. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_deliveries | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2852 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(10)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the names, phone numbers, and emails of all customers sorted by their dates of becoming customers. | spider1_0053 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "customer_name",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the name of the first 5 customers. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_deliveries | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2853 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(10)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the names of the first five customers. | spider1_0054 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "payment_method",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the payment method that is used most frequently. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_deliveries | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2854 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(10)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the payment method that is used most frequently. | spider1_0055 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "actor_last_name_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many different last names do the actors and actresses have? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2924 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "actor_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": "first_name",
"source_type": "VARCHAR(45)"
},
{
"flink_type": "STRING",
"name": "last_na... | Actor data is transmitted in the order of last_update, and finally output as the number of different last names the actors and actresses have. | spider1_0056 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "first_name",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the most popular first name of the actors? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2926 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "actor_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": "first_name",
"source_type": "VARCHAR(45)"
},
{
"flink_type": "STRING",
"name": "last_na... | Actor data is transmitted in the order of last_update, and finally output as the most popular first name of the actors. | spider1_0057 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "district",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Which districts have at least two addresses? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2930 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "address_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": "address",
"source_type": "VARCHAR(50)"
},
{
"flink_type": "STRING",
"name": "address2... | Address data is transmitted in the order of last_update, and finally output as the districts that have at least two addresses. | spider1_0058 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "active_customer_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many customers have an active value of 1? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2946 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "customer_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "SMALLINT",
"name": "store_id",
"source_type": "TINYINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": ... | Customer data is transmitted in the order of create_date, and finally output as the number of customers with an active value of 1. | spider1_0060 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "title",
"position": 1
},
{
"name": "rental_rate",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Which film has the highest rental rate? And what is the rate? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2948 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "film_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": "title",
"source_type": "VARCHAR(255)"
},
{
"flink_type": "STRING",
"name": "description"... | Film data is transmitted in the order of last_update, and finally output as the film with the highest rental rate and the rate itself. | spider1_0061 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "store_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Which store owns most items? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2956 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "inventory_id",
"source_type": "MEDIUMINT UNSIGNED"
},
{
"flink_type": "INT",
"name": "film_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "SMALLINT",
"name"... | Inventory data is transmitted in the order of last_update, and finally output as the store ID that owns the most items. | spider1_0062 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "total_payment_amount",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the total amount of all payments? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2958 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "payment_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "INT",
"name": "customer_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "SMALLINT",
"name": ... | Payment data is transmitted in the order of payment_date, and finally output as the total amount of all payments. | spider1_0063 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "film_language_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many languages are in these films? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2970 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "film_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": "title",
"source_type": "VARCHAR(255)"
},
{
"flink_type": "STRING",
"name": "description"... | Film data is transmitted in the order of last_update, and finally output as the number of distinct languages in these films. | spider1_0064 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "film_rating_category_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many kinds of different ratings are listed? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | sakila_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_2982 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "INT",
"name": "film_id",
"source_type": "SMALLINT UNSIGNED"
},
{
"flink_type": "STRING",
"name": "title",
"source_type": "VARCHAR(255)"
},
{
"flink_type": "STRING",
"name": "description"... | Film data is transmitted in the order of last_update, and finally output as the number of different ratings listed. | spider1_0065 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "assessment_note_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many assessment notes are there in total? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | behavior_monitoring | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3086 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "notes_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "student_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "teacher_id",
... | Assessment note data is transmitted in the order of date_of_notes, and finally output as the total number of assessment notes. | spider1_0066 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "incident_type_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many distinct incident type codes are there? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | behavior_monitoring | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3089 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "incident_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "incident_type_code",
"source_type": "VARCHAR(10)"
},
{
"flink_type": "BIGINT",
"name": "stu... | Behavior incident data is transmitted in the order of date_incident_start, and finally output as the number of distinct incident type codes. | spider1_0067 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "maximum_monthly_rental",
"position": 1
},
{
"name": "minimum_monthly_rental",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the maximum and minimum monthly rental for all student addresses. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | behavior_monitoring | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3098 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "student_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "address_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_address_fro... | Student address data is transmitted in the order of date_address_from, and finally output as the maximum and minimum monthly rental for all student addresses. | spider1_0069 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "student_without_incident_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many students are not involved in any behavior incident? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | behavior_monitoring | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3123 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "student_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "address_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "first_name",
... | Student data is transmitted in the order of date_first_rental, and finally output as the number of students not involved in any behavior incident. | spider1_0071 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "asset_acquired_date",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | When was the first asset acquired? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | assets_maintenance | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3142 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "asset_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "maintenance_contract_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "suppl... | Asset data is transmitted in the order of asset_acquired_date, and finally output as the date when the first asset was acquired. | spider1_0072 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "enrolled_student_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many distinct students are enrolled in courses? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | e_learning | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3780 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "registration_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "student_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "course_id",... | Student course enrolment data is transmitted in the order of date_of_enrolment, and finally output as the number of distinct students enrolled in courses. | spider1_0073 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "failed_test_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many tests have result "Fail"? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | e_learning | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3786 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "registration_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_test_taken",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "test_... | Student test data is transmitted in the order of date_test_taken, and finally output as the number of tests with a result of "Fail". | spider1_0075 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "son_name_student_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many students have personal names that contain the word "son"? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | e_learning | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3792 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "student_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_of_registration",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "date_... | Student data is transmitted in the order of date_of_registration, and finally output as the number of students whose personal names contain the word "son". | spider1_0076 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "test_result",
"position": 1
},
{
"name": "test_result_count",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | List each test result and its count in descending order of count. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | e_learning | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3800 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "registration_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_test_taken",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "test_... | Student test data is transmitted in the order of date_test_taken, and finally output as each test result and its count in descending order of count. | spider1_0077 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "unenrolled_student_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many students did not have any course enrollment? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | e_learning | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3844 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "student_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_of_registration",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "date_... | Student data is transmitted in the order of date_of_registration, and finally output as the number of students who did not have any course enrollment. | spider1_0078 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | any_k_from_tie_set | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "appointment_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the id of the appointment with the most recent start date? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | hospital_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3908 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "AppointmentID",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "Patient",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "PrepNurse",
... | Appointment data is transmitted in the order of Start, and finally output as the ID of the appointment with the most recent start date. | spider1_0079 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "appointment_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many appointments are there? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | hospital_1 | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_3994 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "AppointmentID",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "Patient",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "PrepNurse",
... | Appointment data is transmitted in the order of Start, and finally output as the number of appointments. | spider1_0080 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "calendar_date",
"position": 1
},
{
"name": "day_number",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show all calendar dates and day Numbers. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Doc_Tracking_DB | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4162 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "STRING",
"name": "Calendar_Date",
"source_type": "DATETIME"
},
{
"flink_type": "BIGINT",
"name": "Day_Number",
"source_type": "INTEGER"
}
],
"format": "csv",
"name": "Ref_calendar",
"partition... | Calendar data is transmitted in the order of Calendar_Date, and finally output as all calendar dates and their day numbers. | spider1_0082 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "cv_or_bk_document_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show the number of documents with document type code CV or BK. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Doc_Tracking_DB | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4172 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Document_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Date_Stored",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "Document_Type... | Document data is transmitted in the order of Date_Stored, and finally output as the number of documents with a type code of CV or BK. | spider1_0083 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "role_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the role code with the largest number of employees? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Doc_Tracking_DB | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4206 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Employee_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Role_Code",
"source_type": "CHAR(15)"
},
{
"flink_type": "STRING",
"name": "Employee_Name",... | Employee data is transmitted in the order of Date_of_Birth, and finally output as the role code with the largest number of employees. | spider1_0086 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "role_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show all role codes with at least 3 employees. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Doc_Tracking_DB | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4208 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Employee_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Role_Code",
"source_type": "CHAR(15)"
},
{
"flink_type": "STRING",
"name": "Employee_Name",... | Employee data is transmitted in the order of Date_of_Birth, and finally output as the role codes that have at least three employees. | spider1_0087 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "role_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Return every role code tied for the smallest employee count. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Doc_Tracking_DB | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4210 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Employee_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Role_Code",
"source_type": "CHAR(15)"
},
{
"flink_type": "STRING",
"name": "Employee_Name",... | Employees data is transmitted in ascending Date_of_Birth order; after the complete source stream is consumed, output the final relation defined by the repaired question. | spider1_0088 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "date_from",
"position": 1
},
{
"name": "date_to",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | List from which date and to which date these staff work: project staff of the project which hires the most staffs | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4326 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "DOUBLE",
"name": "staff_id",
"source_type": "DOUBLE"
},
{
"flink_type": "BIGINT",
"name": "project_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "role_code",
... | Project staff data is transmitted in the order of date_from, and finally output as the date ranges for staff working on the project that hires the most staff and for staff with the leader role. | spider1_0089 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "staff_role_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many kinds of roles are there for the staff? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4344 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "DOUBLE",
"name": "staff_id",
"source_type": "DOUBLE"
},
{
"flink_type": "BIGINT",
"name": "project_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "role_code",
... | Project staff data is transmitted in the order of date_from, and finally output as the number of distinct kinds of roles for the staff. | spider1_0090 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "total_grant_amount",
"position": 1
},
{
"name": "organisation_id",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the total amount of grants given by each organisations? Also list the organisation id. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4346 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "grant_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "organisation_id",
"source_type": "INTEGER"
},
{
"flink_type": "DECIMAL(19,4)",
"name": "grant_... | Grant data is transmitted in the order of grant_start_date, and finally output as the total amount of grants given by each organization along with the organization ID. | spider1_0091 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "date_from",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | When did the first staff for the projects started working? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4354 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "DOUBLE",
"name": "staff_id",
"source_type": "DOUBLE"
},
{
"flink_type": "BIGINT",
"name": "project_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "role_code",
... | Project staff data is transmitted in the order of date_from, and finally output as the date when the first staff for the projects started working. | spider1_0092 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "grant_id",
"position": 1
},
{
"name": "maximum_document_count_per_grant",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many documents can one grant have at most? List the grant id and number. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4368 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "document_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "document_type_code",
"source_type": "VARCHAR(10)"
},
{
"flink_type": "BIGINT",
"name": "gra... | Document data is transmitted in the order of sent_date, and finally output as the grant ID and the maximum number of documents a single grant can have. | spider1_0093 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "leader_or_early_start_project_staff_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many project staff worked as leaders or started working before '1989-04-24 23:51:54'? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4376 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "DOUBLE",
"name": "staff_id",
"source_type": "DOUBLE"
},
{
"flink_type": "BIGINT",
"name": "project_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "role_code",
... | Project staff data is transmitted in the order of date_from, and finally output as the number of project staff who worked as leaders or started working before '1989-04-24 23:51:54'. | spider1_0094 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "date_to",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the last date of the staff leaving the projects? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4378 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "DOUBLE",
"name": "staff_id",
"source_type": "DOUBLE"
},
{
"flink_type": "BIGINT",
"name": "project_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "role_code",
... | Project staff data is transmitted in the order of date_from, and finally output as the last date of the staff leaving the projects. | spider1_0095 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "role_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Which role is most common for the staff? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_grants_for_research | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4396 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "DOUBLE",
"name": "staff_id",
"source_type": "DOUBLE"
},
{
"flink_type": "BIGINT",
"name": "project_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "role_code",
... | Project staff data is transmitted in the order of date_from, and finally output as the most common role for the staff. | spider1_0096 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "product_category",
"position": 1
},
{
"name": "category_mailshot_count",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show all distinct product categories along with the number of mailshots in each category. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_campaigns_ecommerce | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4624 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "mailshot_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "product_category",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "mails... | Mailshot campaign data is transmitted in the order of mailshot_start_date, and finally output as all distinct product categories along with the number of mailshots in each category. | spider1_0097 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "outcome_code",
"position": 1
},
{
"name": "outcome_mailshot_count",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show the outcome code of mailshots along with the number of mailshots in each outcome code. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_campaigns_ecommerce | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4627 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "mailshot_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "outcome_code",... | Mailshot customer data is transmitted in the order of mailshot_customer_date, and finally output as the outcome code of mailshots along with the number of mailshots in each outcome code. | spider1_0098 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "product_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What are the ids of the top three products that were purchased in the largest amount? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | department_store | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4709 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "supplier_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_supplied_f... | Product supplier data is transmitted in the order of date_supplied_from, and finally output as the IDs of the top three products purchased in the largest amount. | spider1_0099 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "department_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Return every department ID tied for the smallest number of staff assignments. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | department_store | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4743 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "staff_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "department_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_assigned_f... | staff_department_assignments data is transmitted in ascending date_assigned_from order; after the complete source stream is consumed, output the final relation defined by the repaired question. | spider1_0100 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "customer_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the ids of all distinct customers who made order after some orders that were Cancelled. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | department_store | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4753 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "order_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "order_status_code... | Customer order data is transmitted in the order of order_date, and finally output as the IDs of all distinct customers who made an order after the first cancelled order. | spider1_0101 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "staff_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is id of the staff who had a Staff Department Assignment earlier than any Clerical Staff? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | department_store | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4755 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "staff_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "department_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_assigned_f... | Staff department assignment data is transmitted in the order of date_assigned_from, and finally output as the ID of the staff who had an assignment earlier than any clerical staff. | spider1_0102 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "supplier_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the id of suppliers whose average amount purchased for each product is above 50000 or below 30000. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | department_store | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4771 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "supplier_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_supplied_f... | Product supplier data is transmitted in the order of date_supplied_from, and finally output as the IDs of suppliers whose average amount purchased per product is above 50000 or below 30000. | spider1_0103 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "top_supplier_average_total_amount_purchased",
"position": 1
},
{
"name": "top_supplier_average_total_value_purchased",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Choose the supplier with the largest number of supplied products, breaking ties by the smallest supplier ID, and return that supplier's decimal average total amount purchased and decimal average total value purchased. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | department_store | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4773 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "supplier_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_supplied_f... | Product_Suppliers data is transmitted in ascending date_supplied_from order; after the complete source stream is consumed, output the final relation defined by the repaired question. | spider1_0104 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Transportation, Urban & Government | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "thing_status_code_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many different status codes of things are there? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | local_govt_and_lot | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4853 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "thing_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Date_and_Date",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "Status_of_Thin... | Timed status data is transmitted in the order of Date_and_Date, and finally output as the number of different status codes of things. | spider1_0105 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Transportation, Urban & Government | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "latest_resident_move_in_date",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | When is the last day any resident moved in? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | local_govt_and_lot | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_4855 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "resident_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "property_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_moved_in"... | Resident data is transmitted in the order of date_moved_in, and finally output as the last day any resident moved in. | spider1_0106 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "booking_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many bookings do we have? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Drama_Workshop_Groups | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5090 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Booking_ID",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "Customer_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Workshop_Group_... | Booking data is transmitted in the order of Order_Date, and finally output as the number of bookings we have. | spider1_0107 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "status_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the most frequent status of bookings? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Drama_Workshop_Groups | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5160 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Booking_ID",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "Customer_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Workshop_Group_... | Booking data is transmitted in the order of Order_Date, and finally output as the most frequent status of bookings. | spider1_0108 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Software & Developer Ecosystems | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "problem_log_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the id of the problem log that is created most recently? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_software_problems | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5352 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "problem_log_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "assigned_to_staff_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "pr... | Problem log data is transmitted in the order of log_entry_date, and finally output as the ID of the most recently created problem log. | spider1_0109 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Software & Developer Ecosystems | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "problem_log_id",
"position": 1
},
{
"name": "problem_id",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the oldest log id and its corresponding problem id? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_software_problems | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5354 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "problem_log_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "assigned_to_staff_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "pr... | Problem log data is transmitted in the order of log_entry_date, and finally output as the oldest log ID and its corresponding problem ID. | spider1_0110 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "product_name",
"position": 1
},
{
"name": "product_category_code",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the name and category of the most expensive product. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_complaints | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5788 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "parent_product_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "product_c... | Product data is transmitted in the order of date_product_first_available, and finally output as the name and category of the most expensive product. | spider1_0111 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "product_category_code",
"type": "STRING"
},
{
"aliases": [
"avg_price",
"avg(product_price)"
],
"name": "average_price",
"type": "DECIMAL"
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the average price of the products for each category? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_complaints | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5792 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "parent_product_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "product_c... | Product data is transmitted in the order of date_product_first_available, and finally output as the average price of the products for each category. | spider1_0112 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "complaint_status_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Which complaint status has more than 3 records on file? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_complaints | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5796 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "complaint_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
... | Complaint data is transmitted in the order of date_complaint_raised, and finally output as the complaint status codes that have more than three records on file. | spider1_0113 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "complaint_type_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many distinct complaint type codes are there in the database? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_complaints | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5804 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "complaint_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
... | Complaint data is transmitted in the order of date_complaint_raised, and finally output as the number of distinct complaint type codes. | spider1_0114 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "complaint_status_code",
"position": 1
},
{
"name": "product_failure_complaint_count",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the number of complaints with Product Failure type for each complaint status. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customer_complaints | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5808 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "complaint_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "product_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
... | Complaint data is transmitted in the order of date_complaint_raised, and finally output as the number of Product Failure type complaints for each complaint status. | spider1_0115 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Finance & Digital Assets | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "minimum_purchase_transaction_amount",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show the minimum transaction amount among PUR transactions whose share_count is a valid integer greater than 50; exclude blank or non-integer share_count values. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_share_transactions | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5854 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "transaction_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "investor_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "transaction... | TRANSACTIONS data is transmitted in ascending date_of_transaction order; after the complete source stream is consumed, output the final relation defined by the repaired question. | spider1_0120 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Finance & Digital Assets | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "transaction_type_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many distinct transaction types are used in the transactions? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_share_transactions | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5859 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "transaction_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "investor_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "transaction... | Transaction data is transmitted in the order of date_of_transaction, and finally output as the number of distinct transaction types used. | spider1_0122 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Finance & Digital Assets | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "transaction_type_code",
"position": 1
},
{
"name": "transaction_type_average_amount",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show the average transaction amount for different transaction types. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_share_transactions | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5866 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "transaction_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "investor_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "transaction... | Transaction data is transmitted in the order of date_of_transaction, and finally output as the average transaction amount for each transaction type. | spider1_0123 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Finance & Digital Assets | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "transaction_type_code",
"position": 1
},
{
"name": "transaction_type_maximum_share_count",
"position": 2
},
{
"name": "transaction_type_minimum_share_count",
"position": 3
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | For each transaction type, show the numeric maximum and minimum valid integer share_count, excluding blank or non-integer share_count values. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_share_transactions | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_5867 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "transaction_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "investor_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "transaction... | TRANSACTIONS data is transmitted in ascending date_of_transaction order; after the complete source stream is consumed, output the final relation defined by the repaired question. | spider1_0124 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "customer_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many customers are there? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6057 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the number of customers. | spider1_0125 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "payment_method",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Which payment method is used by most customers? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6065 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the payment method used by most customers. | spider1_0126 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "customer_name",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What are the names of customers using the most popular payment method? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6067 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "payment_method",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "custome... | Customer data is transmitted in the order of date_became_customer, and finally output as the names of customers using the most popular payment method. | spider1_0127 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "channel_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | List all the contact channel codes that were used less than 5 times. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6095 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "channel_code",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "active_fr... | Customer contact channel data is transmitted in the order of active_from_date, and finally output as the list of contact channel codes used less than five times. | spider1_0128 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "avg_elapsed_seconds",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the average actual elapsed duration, in seconds, from active_from_date to active_to_date across all contact channels? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6101 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "channel_code",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "active_fr... | Customer contact channel rows are transmitted in ascending active_from_date order; after the complete source stream is consumed, output the final average elapsed duration in seconds. | spider1_0129 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "channel_code",
"position": 1
},
{
"name": "contact_number",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Return the channel code and contact number for every contact channel tied for the largest actual elapsed duration in seconds from active_from_date to active_to_date. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6103 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "channel_code",
"source_type": "VARCHAR(15)"
},
{
"flink_type": "STRING",
"name": "active_fr... | customer_contact_channels data is transmitted in ascending active_from_date order; after the complete source stream is consumed, output the final relation defined by the repaired question. | spider1_0130 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "cancelled_order_customer_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many customers have at least one order with status "Cancelled"? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | customers_and_addresses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6117 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "order_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "order_status",
... | Customer order data is transmitted in the order of order_date, and finally output as the number of customers with at least one order with status "Cancelled". | spider1_0131 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Transportation, Urban & Government | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "organization_name",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the name of the youngest organization. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | e_government | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6320 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "organization_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "date_formed",
"source_type": "DATETIME"
},
{
"flink_type": "STRING",
"name": "organizat... | Organization data is transmitted in the order of date_formed, and finally output as the name of the youngest organization. | spider1_0132 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "project_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the id of the project with least number of documents? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Docs_and_Epenses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6413 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Document_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Document_Type_Code",
"source_type": "CHAR(15)"
},
{
"flink_type": "BIGINT",
"name": "Projec... | Document data is transmitted in the order of Document_Date, and finally output as the ID of the project with the least number of documents. | spider1_0136 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "project_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Show the ids for projects with at least 2 documents. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Docs_and_Epenses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6415 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Document_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Document_Type_Code",
"source_type": "CHAR(15)"
},
{
"flink_type": "BIGINT",
"name": "Projec... | Document data is transmitted in the order of Document_Date, and finally output as the IDs for projects with at least two documents. | spider1_0137 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "document_type_code",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the document type code with most number of documents? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | cre_Docs_and_Epenses | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6419 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "Document_ID",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "Document_Type_Code",
"source_type": "CHAR(15)"
},
{
"flink_type": "BIGINT",
"name": "Projec... | Document data is transmitted in the order of Document_Date, and finally output as the document type code with the most number of documents. | spider1_0139 | upstream | {
"D": "D2",
"S": "S2",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "first_name",
"position": 1
},
{
"name": "middle_name",
"position": 2
},
{
"name": "last_name",
"position": 3
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | List the first name middle name and last name of all staff. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | driving_school | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6628 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "staff_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "staff_address_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "nickname",
... | Staff data is transmitted in the order of date_of_birth, and finally output as the first name, middle name, and last name of all staff. | spider1_0141 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "ludie_staff_count",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | How many staff have the first name Ludie? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | driving_school | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6636 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "staff_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "staff_address_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "nickname",
... | Staff data is transmitted in the order of date_of_birth, and finally output as the number of staff with the first name Ludie. | spider1_0142 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D1 | easy | easy | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "first_name",
"position": 1
},
{
"name": "last_name",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | List the first name and last name of all customers. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | driving_school | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6658 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_address_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "custom... | Customer data is transmitted in the order of date_became_customer, and finally output as the first name and last name of all customers. | spider1_0144 | upstream | {
"D": "D1",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "order_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | What is the id of the most recent order? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_orders | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6885 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "order_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "order_status",
... | Order data is transmitted in the order of date_order_placed, and finally output as the ID of the most recent order. | spider1_0145 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "order_id",
"position": 1
},
{
"name": "customer_id",
"position": 2
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | what are the order id and customer id of the oldest order? | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_orders | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6887 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "order_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "customer_id",
"source_type": "INTEGER"
},
{
"flink_type": "STRING",
"name": "order_status",
... | Order data is transmitted in the order of date_order_placed, and finally output as the order ID and customer ID of the oldest order. | spider1_0146 | upstream | {
"D": "D2",
"S": "S1",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
null | exact_multiset | D2 | medium | medium | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | ascending_event_time_stable_ties | [
{
"name": "order_id",
"position": 1
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Find the id of the order which is shipped most recently. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | Spider | tracking_orders | null | Apache-2.0 in the official taoyds/spider repository | train_spider | train_spider.json_6923 | https://yale-lily.github.io/spider | [
{
"columns": [
{
"flink_type": "BIGINT",
"name": "shipment_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "order_id",
"source_type": "INTEGER"
},
{
"flink_type": "BIGINT",
"name": "invoice_number",
... | Shipment data is transmitted in the order of shipment_date, and finally output as the ID of the order which was shipped most recently. | spider1_0147 | upstream | {
"D": "D2",
"S": "S3",
"T": "T1"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "from_status_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "to_status_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "transition_count",
... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original order_history row at status_date. Within each order, order rows by status_date then history_id and form exactly adjacent pairs with different status_id values. For every directed status transition with at least 100 pairs, count all pairs and those occurring strictly later within 24 hours. Return ... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | books | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_books_24h_order_status_transition_rate_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "history_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "order_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "status_id",
"sqlite_type": "INTEGER",
"type": ... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`order_history.status_date`). The final output is the result requested by the task. | authored_bird_books_24h_order_status_transition_rate_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "shipping_method_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "order_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "cascade_order_count"... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original customer-order and order-history row at order_date or status_date. For every shipping method with at least 500 orders, count all orders and those whose first status event strictly after order creation occurs within eight hours and is followed strictly later within 24 hours by a different status. ... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | books | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_books_8h24h_shipping_status_cascade_rate_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "order_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "order_date",
"sqlite_type": "DATETIME",
"type": "STRING"
},
{
"name": "shipping_method_id",
"sqlite_type": "INTEGER",
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`cust_order.order_date`; `order_history.status_date`). The final output is the result requested by the task. | authored_bird_books_8h24h_shipping_status_cascade_rate_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Software & Developer Ecosystems | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "badge_name",
"nullable": false,
"numeric_mode": "exact",
"type": "STRING",
"value_kind": "string"
},
{
"name": "user_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "timely_user_count",
"null... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original user and badge row at its unchanged CreationDate or Date. For each user, select the first badge by Date then badge Id. It is timely only when awarded strictly after account creation and no later than 24 hours afterward. For each first-badge name, count users and timely users; keep badge names obs... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | codebase_community | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_codebase_24h_first_badge_signup_rate_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "Id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "CreationDate",
"sqlite_type": "DATETIME",
"type": "STRING"
}
],
"format": "jsonl",
"name": "users",
"partition_zero_directly_observed": ... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`users.CreationDate`; `badges.Date`). The final output is the result requested by the task. | authored_bird_codebase_24h_first_badge_signup_rate_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Software & Developer Ecosystems | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "post_day",
"nullable": false,
"numeric_mode": "exact",
"type": "STRING",
"value_kind": "string"
},
{
"name": "qualifying_post_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "following_comment_co... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original post and comment row at its unchanged creation time. A post has an early diverse discussion when at least two comments from at least two distinct non-NULL users occur strictly after post creation and within 60 minutes. For each UTC post-creation day, count qualifying posts and their following com... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | codebase_community | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_codebase_60m_new_post_discussion_day_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "Id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "CreaionDate",
"sqlite_type": "DATETIME",
"type": "STRING"
}
],
"format": "jsonl",
"name": "posts",
"partition_zero_directly_observed": t... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`posts.CreaionDate`; `comments.CreationDate`). The final output is the result requested by the task. | authored_bird_codebase_60m_new_post_discussion_day_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Software & Developer Ecosystems | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "history_day",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "history_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "post_id",
"nullable": false,
"numeric_mode... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original postHistory row and every original comments row in their respective CreationDate order. A comment follows a history event only when it belongs to the same PostId, occurs strictly after the history event, and occurs no later than 30 minutes afterward. Keep history events followed by at least two c... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | codebase_community | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_codebase_history_comment_response | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "Id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "PostId",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "CreationDate",
"sqlite_type": "DATETIME",
"type": "STRIN... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`postHistory.CreationDate`; `comments.CreationDate`). The final output is the result requested by the task. | authored_bird_codebase_history_comment_response | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Commerce & Customer Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "item_row_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "updated_within_24h_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "updated_aft... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original MenuItem row when the later of its unchanged created_at and updated_at timestamps is available. Return the total item-row count, the count updated strictly after creation and within 24 hours, and the count updated after 24 hours but within 30 days. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | menu | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_menuitem_1d30d_update_latency_count | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "created_at",
"sqlite_type": "TEXT",
"type": "STRING"
},
{
"name": "updated_at",
"sqlite_type": "TEXT",
"type": "STRING"
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `MenuItem.updated_at`, `MenuItem.created_at`). The final output is the result requested by the task. | authored_bird_menuitem_1d30d_update_latency_count | curator | {
"D": "D3",
"S": "S3",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "category_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "rental_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "joint_completion_count",
... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Load the complete original inventory and film-category relations as static identity mappings before live events. Then process every original rental and payment row at its original availability clock, releasing a rental at the later non-NULL one of rental_date and return_date. For every film category with at least 500 r... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_3 | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie3_10m48h_category_payment_return_rate_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "rental_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "rental_date",
"sqlite_type": "DATETIME",
"type": "STRING"
},
{
"name": "return_date",
"sqlite_type": "DATETIME",
"t... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `rental.return_date`, `rental.rental_date`; `payment.payment_date`). The final output is the result requested by the task. | authored_bird_movie3_10m48h_category_payment_return_rate_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "rental_row_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "within_1d_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "after_1d_within_3d... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original rental row when the later of rental_date and return_date is available. Return all rental rows, positive-duration rentals returned within one day, and positive-duration rentals returned after one day but within three days. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_3 | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie3_1d3d_rental_return_duration_count | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "rental_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "rental_date",
"sqlite_type": "DATETIME",
"type": "STRING"
},
{
"name": "return_date",
"sqlite_type": "DATETIME",
"t... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `rental.return_date`, `rental.rental_date`). The final output is the result requested by the task. | authored_bird_movie3_1d3d_rental_return_duration_count | curator | {
"D": "D3",
"S": "S3",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "customer_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "completed_rental_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "rebooking_count"... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original rental row when its return_date becomes available, falling back to rental_date only for rows with no return. For each completed rental, mark a rebooking when the same customer starts another rental strictly after return and within 24 hours. For each customer, count completed rentals and rebooking... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_3 | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie3_24h_return_rebooking_customer_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "rental_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "customer_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "rental_date",
"sqlite_type": "DATETIME",
"ty... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `rental.return_date`, `rental.rental_date`). The final output is the result requested by the task. | authored_bird_movie3_24h_return_rebooking_customer_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "customer_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "low_payment_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "upsell_response_count... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original payment row at its unchanged payment_date. Treat each payment of at most 3 as a low-payment anchor. It receives an upsell response when the same customer makes a payment of at least 8 strictly later and within seven days. For each customer, count low anchors and upsell responses; keep customers w... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_3 | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie3_7d_payment_upsell_customer_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "payment_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "customer_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "amount",
"sqlite_type": "REAL",
"type": "DO... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`payment.payment_date`). The final output is the result requested by the task. | authored_bird_movie3_7d_payment_upsell_customer_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "rental_day",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "category_name",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "late_rental_count",
"nullable": false,
"numeric_mode": "exact"... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original rental lifecycle row and payment row and the complete inventory, film, film-category, and category relations. A completed rental lifecycle row arrives at its original return_date; an open rental with NULL return_date arrives at its original rental_date. A rental is late only when its return time ... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_3 | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie3_daily_late_return_revenue_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "rental_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "rental_date",
"sqlite_type": "DATETIME",
"type": "STRING"
},
{
"name": "return_date",
"sqlite_type": "DATETIME",
"t... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `rental.return_date`, `rental.rental_date`; `payment.payment_date`). The final output is the result requested by the task. | authored_bird_movie3_daily_late_return_revenue_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "user_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "list_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "timely_update_count",
"nulla... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original list row at the later of its unchanged creation and update timestamps. A list is timely updated only when its update is strictly after creation and no later than 24 hours afterward. For each user, count all lists and timely updated lists; keep users with at least five lists. Return every user in ... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_platform | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie_lists_24h_update_user_rate_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "user_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "list_creation_timestamp_utc",
"sqlite_type": "TEXT",
"type": "STRING"
},
{
"name": "list_update_timestamp_utc",
"sqlite_type"... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `lists.list_update_timestamp_utc`, `lists.list_creation_timestamp_utc`). The final output is the result requested by the task. | authored_bird_movie_lists_24h_update_user_rate_rank | curator | {
"D": "D3",
"S": "S3",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "rating_month",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "movie_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "first_week_count",
"nullable": false,
"nume... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original rating row in its original rating_timestamp_utc order. For each UTC calendar month and movie, count non-NULL-score ratings in days 1-7 as first_week_count and days 22-28 as fourth_week_count. Keep movie-months with at least five ratings in each period and with fourth_week_count greater than first... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | movie_platform | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_movie_platform_monthly_rating_growth | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "movie_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "rating_score",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "rating_timestamp_utc",
"sqlite_type": "TEXT",
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`ratings.rating_timestamp_utc`). The final output is the result requested by the task. | authored_bird_movie_platform_monthly_rating_growth | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Media, Culture & Social Platforms | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "review_month",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "review_day",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "podcast_id",
"nullable": false,
"numeric_mode": "exact",
"v... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original podcast review in its original created_at order. Use the local calendar date encoded by the original -07:00 timestamp; normalize only Unicode decimal digit glyphs when interpreting the clock, without changing the source value. For each podcast and local day, count reviews, distinct authors, ratin... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | music_platform_2 | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_music_daily_negative_skew_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "podcast_id",
"sqlite_type": "TEXT",
"type": "STRING"
},
{
"name": "rating",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "author_id",
"sqlite_type": "TEXT",
"type": "STRING"... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`reviews.created_at`). The final output is the result requested by the task. | authored_bird_music_daily_negative_skew_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | General Enterprise Operations | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "rapid_pair_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "device_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original device event at timestamp. For each non-NULL device, order events by timestamp then event_id and form only exactly adjacent pairs. Count pairs whose second event is strictly later and within five minutes, and devices with at least one rapid pair. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | talkingdata | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_talkingdata_5m_adjacent_device_activity_count | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "event_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "device_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "timestamp",
"sqlite_type": "DATETIME",
"type": ... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`events.timestamp`). The final output is the result requested by the task. | authored_bird_talkingdata_5m_adjacent_device_activity_count | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
coupled_high | exact_multiset | D3 | hard_advanced | hard | Transportation, Urban & Government | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "bucket_start",
"nullable": false,
"numeric_mode": "exact",
"value_kind": "string"
},
{
"name": "device_id",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "located_event_count",
"nullable": false,
"... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original device event in its original timestamp order. Treat an event as located unless both longitude and latitude are zero. For each UTC-aligned ten-minute bucket and device, count located events and measure the longitude and latitude ranges. Keep device-buckets with at least three located events and wi... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | BIRD | talkingdata | null | CC-BY-SA-4.0 for benchmark annotations; underlying database components retain their own terms | database | curator:authored_bird_talkingdata_ten_minute_mobility_rank | https://bird-bench.github.io/ | [
{
"columns": [
{
"name": "event_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "device_id",
"sqlite_type": "INTEGER",
"type": "BIGINT"
},
{
"name": "timestamp",
"sqlite_type": "DATETIME",
"type": ... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`events.timestamp`). The final output is the result requested by the task. | authored_bird_talkingdata_ten_minute_mobility_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "patientunitstayid",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "qualifying_window_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "qualifyin... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original vitalPeriodic row at its unchanged observationtime. For each ICU stay and UTC-aligned ten-minute window, count rows, count how many of temperature, oxygen saturation, heart rate, respiration, systemic systolic pressure and systemic diastolic pressure appear at least once, and compute the non-NULL... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | EHRSQL | eicu | [
"eICU",
"eicu"
] | CC-BY-4.0 for EHRSQL; MIMIC/eICU source access has separate terms | database | curator:authored_eicu_10m_vital_coverage_heartrate_range_stay_rank | https://github.com/glee4810/EHRSQL | [
{
"columns": [
{
"name": "vitalperiodicid",
"sqlite_type": "BIGINT",
"type": "BIGINT"
},
{
"name": "patientunitstayid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "temperature",
"sqlite_type": "NUMERIC(11,4)",... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`vitalPeriodic.observationtime`). The final output is the result requested by the task. | authored_eicu_10m_vital_coverage_heartrate_range_stay_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "escalation_pair_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "stay_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
}
] | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original vitalperiodic row at observationtime. Count ordered pairs in the same ICU stay where a heart rate at most 60 is followed strictly later and within one hour by a heart rate at least 100. Also return the number of ICU stays with at least one such escalation pair. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | EHRSQL | eicu | [
"eICU",
"eicu"
] | CC-BY-4.0 for EHRSQL; MIMIC/eICU source access has separate terms | database | curator:authored_eicu_1h_hr_escalation_stay_count | https://github.com/glee4810/EHRSQL | [
{
"columns": [
{
"name": "vitalperiodicid",
"sqlite_type": "BIGINT",
"type": "BIGINT"
},
{
"name": "patientunitstayid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "heartrate",
"sqlite_type": "INT",
"ty... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`vitalperiodic.observationtime`). The final output is the result requested by the task. | authored_eicu_1h_hr_escalation_stay_count | curator | {
"D": "D3",
"S": "S3",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "celllabel",
"nullable": false,
"numeric_mode": "exact",
"type": "STRING",
"value_kind": "string"
},
{
"name": "qualifying_window_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "qualifying_row_co... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original intakeOutput row at its unchanged intakeoutputtime. For each cell label and UTC-aligned one-hour window, count rows and distinct ICU stays and compute the range of non-NULL numeric values. A window qualifies when it contains at least 5 rows from at least 2 stays and its numeric-value range is at ... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | EHRSQL | eicu | [
"eICU",
"eicu"
] | CC-BY-4.0 for EHRSQL; MIMIC/eICU source access has separate terms | database | curator:authored_eicu_1h_intakeoutput_value_stay_diversity_label_rank | https://github.com/glee4810/EHRSQL | [
{
"columns": [
{
"name": "intakeoutputid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "patientunitstayid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "celllabel",
"sqlite_type": "VARCHAR(255)",
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`intakeOutput.intakeoutputtime`). The final output is the result requested by the task. | authored_eicu_1h_intakeoutput_value_stay_diversity_label_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "patientunitstayid",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "qualifying_window_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "qualifyin... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original treatment row at its unchanged treatmenttime. Within each ICU stay and UTC-aligned one-hour window, count each treatment name, the total treatment rows, the number of distinct names and the largest same-name repetition count. A window qualifies when it contains at least 20 distinct treatment name... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | EHRSQL | eicu | [
"eICU",
"eicu"
] | CC-BY-4.0 for EHRSQL; MIMIC/eICU source access has separate terms | database | curator:authored_eicu_1h_treatment_name_repeat_diversity_stay_rank | https://github.com/glee4810/EHRSQL | [
{
"columns": [
{
"name": "treatmentid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "patientunitstayid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "treatmentname",
"sqlite_type": "VARCHAR(200)",
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`treatment.treatmenttime`). The final output is the result requested by the task. | authored_eicu_1h_treatment_name_repeat_diversity_stay_rank | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "medication_row_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "within_2h_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "after_2h_withi... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original medication row when the later of drugstarttime and drugstoptime is available. Return all medication rows, positive-duration courses lasting at most two hours, and positive-duration courses lasting more than two hours but at most twelve hours. | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | EHRSQL | eicu | [
"eICU",
"eicu"
] | CC-BY-4.0 for EHRSQL; MIMIC/eICU source access has separate terms | database | curator:authored_eicu_2h12h_medication_course_duration_count | https://github.com/glee4810/EHRSQL | [
{
"columns": [
{
"name": "medicationid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "drugstarttime",
"sqlite_type": "TIMESTAMP(0)",
"type": "STRING"
},
{
"name": "drugstoptime",
"sqlite_type": "TIMESTAMP(0)",
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (the declared timestamp adapter over `medication.drugstoptime`, `medication.drugstarttime`). The final output is the result requested by the task. | authored_eicu_2h12h_medication_course_duration_count | curator | {
"D": "D3",
"S": "S3",
"T": "T3"
} | streamsqlbench_tsd3_v1 |
high | exact_multiset | D3 | hard | hard | Healthcare, Education & Public Services | final_only | {
"evaluation": "final_materialized_csv_only",
"input": "Kafka",
"input_topic_contract": "one_single_partition_topic_per_declared_source",
"mode": "STREAMING",
"output": "Kafka",
"output_record_kind": "changelog"
} | global_merged_nondecreasing_event_time_stable_ties | [
{
"name": "chain_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "entity_count",
"nullable": false,
"numeric_mode": "exact",
"type": "BIGINT",
"value_kind": "number"
},
{
"name": "anchor_count",
"nullab... | [
"reference_sql",
"gold_csv",
"gold_json",
"python_oracle",
"test_cases",
"counterfactuals"
] | Process every original eICU diagnosis, treatment, and periodic-vital row at their original event clocks. A diagnosis name containing pneumonia is an anchor. It completes a chain only when an oxygen-named treatment in the same ICU stay occurs strictly later within two hours and a SaO2 result of at least 95 occurs strict... | redistributed_with_attribution | streamsqlbench-v1.0.0 | STREAMING | EHRSQL | eicu | [
"eICU",
"eicu"
] | CC-BY-4.0 for EHRSQL; MIMIC/eICU source access has separate terms | database | curator:authored_eicu_2h4h_pneumonia_oxygen_sao2_chain_count | https://github.com/glee4810/EHRSQL | [
{
"columns": [
{
"name": "diagnosisid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "patientunitstayid",
"sqlite_type": "INT",
"type": "BIGINT"
},
{
"name": "diagnosisname",
"sqlite_type": "VARCHAR(200)",
... | All rows from each live source are transmitted in nondecreasing order of that source's declared event-time field or adapter (`diagnosis.diagnosistime`; `treatment.treatmenttime`; `vitalperiodic.observationtime`). The final output is the result requested by the task. | authored_eicu_2h4h_pneumonia_oxygen_sao2_chain_count | curator | {
"D": "D3",
"S": "S4",
"T": "T2"
} | streamsqlbench_tsd3_v1 |
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