Guidance on when ClickHouse outperforms ParadeDB for analytic workloads
Choose ClickHouse when ultra-fast, columnar analytics on terabytes of immutable data is your top priority. Its vectorized execution, compression, and distributed MergeTree engine deliver millisecond queries that ParadeDB’s row-store extension cannot match at scale.
Stick with ParadeDB for smaller datasets (<100 GB), transactional joins, or when you need Postgres extensions, roles, and ACID semantics in one cluster.It shines for hybrid transactional/analytical processing where latency <1 s is acceptable.
High-cardinality event logs, real-time dashboards, and funnel analysis run faster because ClickHouse stores columns separately, skips unnecessary blocks, and parallelizes scans across shards.
Batch-insert millions of rows per second in ClickHouse using INSERT … VALUES or INSERT … SELECT.ParadeDB inherits Postgres’s slower single-row INSERT but supports COPY for bulk loads; performance still lags columnar storage.
Yes. Use clickhouse-fdw in Postgres or the Postgres table engine in ClickHouse to query across systems. This hybrid pattern keeps hot OLTP data in ParadeDB while offloading heavy reads to ClickHouse.
Start with one analytic table. Mirror writes to ParadeDB and ClickHouse via logical replication or Kafka.Validate query parity, then cut over read traffic once latency goals are met.
Ignoring sort keys. Without ORDER BY in MergeTree, ClickHouse can’t skip data. Always define a compound key (e.g., (customer_id, order_date)).
Using default compression. ZSTD usually halves disk versus LZ4; set SET compression_codec_zstd
before creating tables.
-- ClickHouse analytic table
CREATE TABLE Orders_MV ENGINE = MergeTree
ORDER BY (customer_id, order_date)
AS SELECT id, customer_id, order_date, total_amount FROM Orders;.
-- ParadeDB GIN index on JSONB
CREATE INDEX idx_orders_total ON Orders USING GIN ((data->'total_amount'));
No. It provides per-part atomicity but lacks multi-statement ACID. Use ParadeDB or Postgres for strict transactions.
ParadeDB relies on TOAST and extensions; compression ratios rarely exceed 3-4×, whereas ClickHouse routinely achieves 10-15×.
Usually yes. ClickHouse nodes are optimized for SSDs and memory bandwidth.Running it side-by-side with Postgres avoids resource contention.
.
Yes, Apache 2.0 licensed with an active community and commercial support.
Possible for testing, but production setups separate them to avoid resource contention.
No, it remains row-oriented but adds vector indexes and search functions on top of Postgres.