Execute a read-only SQL query against ClickHouse.

Runs SELECT queries with automatic safety validation (DDL/DML blocked), LIMIT enforcement, and partition pruning analysis. Returns results with a warning if the query doesn't leverage partitioning efficiently.

Args: sql: SELECT query to execute database: Target database (default: cdc_pipeline) max_rows: Maximum rows to return (default: 1000, max: 10000)

Analyze a query's execution plan and suggest optimizations.

Runs EXPLAIN PLAN and EXPLAIN PIPELINE, then provides structured analysis:

• Whether partition pruning is active

• Whether sorting keys are utilized

• Specific optimization suggestions (add time filters, use pre-aggregated tables, etc.)

This is the key differentiator from generic ClickHouse tools — it doesn't just execute queries, it advises on how to write better ones.

Args: sql: The SELECT query to analyze database: Target database (default: cdc_pipeline)

Get comprehensive table metadata: columns, engine, keys, partitions, and sample data.

Returns column types, partition/sorting/primary keys, TTL settings, row counts, disk usage, partition breakdown, and 5 sample rows. Essential for understanding table structure before writing queries.

Args: table: Table name database: Database name (default: cdc_pipeline)

Analyze CDC pipeline latency by segment.

Measures latency at each stage of the pipeline:

• source_to_cdc: Upbit → Debezium/Kafka

• cdc_to_flink: Kafka → Flink processing

• flink_to_insert: Flink → ClickHouse write

• end_to_end: total source_ts → inserted_at

Each segment reports p50, p95, p99, and max latency in milliseconds. Also includes data freshness (seconds behind real-time) and per-market breakdown.

Args: market: Filter by market (e.g., "KRW-BTC"). Empty = all markets period: Time window — "10m", "1h", "6h", or "24h" (default: "1h")

Run data quality checks: nulls, duplicates, gaps, and market coverage.

Checks for a specific date:

• Null/empty values per column

• Duplicate rows by primary key

• Hourly data gaps (missing time windows)

• Market coverage (are all 5 coins present?)

• Data freshness

Args: table: Table to check (default: crypto_trades) database: Database name (default: cdc_pipeline) check_date: Date to check in YYYY-MM-DD format (default: today)

Find slow queries with root cause diagnosis.

Scans system.query_log for queries exceeding the duration threshold, then generates a diagnosis for each:

• Full scan detection (high read_rows without partition pruning)

• Memory-intensive query detection

• Aggregation optimization suggestions (use pre-aggregated tables)

• Error detection

Args: hours: Look back period in hours (default: 24) min_duration_ms: Minimum query duration to report (default: 1000ms) limit: Maximum number of slow queries to return (default: 20)

Analyze disk usage by table and partition with recommendations.

Returns:

• Total disk usage for the database

• Per-table breakdown (rows, size, parts, percentage)

• Per-partition breakdown (top 20 by size)

• TTL information

• Recommendations (excessive parts, missing TTL on large tables)

Args: database: Database to analyze (default: cdc_pipeline)

List all tables with metadata and built-in descriptions.

Returns table name, engine type, partition/sorting keys, row count, disk size, and a human-readable description of each table's purpose. Use this as the starting point to understand what data is available.

Args: database: Database to list (default: cdc_pipeline)