clickhouse-io

Use the MergeTree engine with a time-based partition key like toYYYYMM(date) and an ORDER BY clause placing frequently filtered columns first. Choose the smallest appropriate data types and LowCardinality for repeated strings to improve compression.

Filter on indexed columns from the ORDER BY key first, avoid SELECT *, and use ClickHouse-specific functions like quantile instead of percentile. Check system.query_log for queries exceeding duration thresholds and review partition pruning behavior.

ReplacingMergeTree deduplicates rows with the same sorting key during background merges, making it suitable for data arriving from multiple sources. Standard MergeTree keeps all inserted rows and is the default choice for append-only analytics data.

Yes, use an ETL pattern that extracts rows from PostgreSQL, transforms them into ClickHouse-compatible types, and batch inserts them. For ongoing sync, implement change data capture using PostgreSQL LISTEN/NOTIFY to stream updates into ClickHouse.

ClickHouse is optimized for batch inserts because each insert creates data parts that require background merges. Insert rows in large batches or use streaming inserts rather than looping single-row INSERT statements.

Use materialized views with AggregatingMergeTree to maintain pre-aggregated metrics like hourly stats in real time as data arrives. Query them with merge functions such as sumMerge and uniqMerge instead of aggregating raw events repeatedly.