Add TsFile (converted from Snaseem2026/devops-predictive-logs)

README.md

@@ -0,0 +1,86 @@
+---
+license: mit
+task_categories:
+- time-series-forecasting
+- text-classification
+tags:
+- tsfile
+- timeseries
+- time-series
+- devops
+- logs
+- incident-prediction
+- sre
+- monitoring
+- format:tsfile
+pretty_name: DevOps Predictive Logs Dataset
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: devops_predictive_logs_train.tsfile
+  - split: test
+    path: devops_predictive_logs_test.tsfile
+---
+
+# DevOps Predictive Logs Dataset (TsFile)
+
+Apache TsFile version of [`Snaseem2026/devops-predictive-logs`](https://huggingface.co/datasets/Snaseem2026/devops-predictive-logs).
+
+## Overview
+
+A synthetic dataset of realistic DevOps log sequences for training and
+benchmarking predictive failure models. Each log entry describes one
+infrastructure observation (service, pod, level, message) inside one of 10
+failure scenarios together with its incident metadata: severity, whether the
+pod eventually fails, and the time-to-failure in minutes.
+
+- **Rows:** 107 logs total — official train split 86 rows, test split 21 rows.
+- **Scenarios:** 10 unique failure scenarios across 12 services / 13 pods.
+- The repo additionally ships `devops_logs_dataset.parquet`, which is exactly
+  the concatenation of the train and test splits (verified row-for-row), so
+  only the two official splits are converted here.
+
+## Schema (TsFile structure)
+
+Both splits share one schema; each split is its own `.tsfile`.
+
+- **Time** (INT64, milliseconds) — log timestamp (`%Y-%m-%d %H:%M:%S`, naive).
+- **pod** (TAG, STRING) — the pod emitting the log stream.
+- **level, service, message, scenario, issue_type, severity,
+  dataset_version, created_date** (FIELD, STRING)
+- **will_fail** (FIELD, BOOLEAN)
+- **time_to_failure_minutes** (FIELD, DOUBLE)
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("devops_predictive_logs_test.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```
+
+## Source & license
+
+- Original dataset: <https://huggingface.co/datasets/Snaseem2026/devops-predictive-logs>
+- License: MIT