How to Choose ClickHouse over Redshift in PostgreSQL

Why choose ClickHouse over Redshift for sub-second analytics?

ClickHouse is built in C++ for single-server speed and MPP scale. Its vectorized execution, primary key ordered storage, and sparse indexes let dashboards return results in milliseconds without result caching.

Redshift’s PostgreSQL 8.0 fork still relies on disk-based execution and VACUUM maintenance. Concurrency remains limited, so OLAP queries spike queue times during traffic peaks.

How does ClickHouse handle real-time inserts?

ClickHouse accepts millions of INSERT rows per second through asynchronous batching. New records become queryable instantly because data is written in immutable parts; no VACUUM or ANALYZE needed.

Redshift’s commit model requires sorted distribution keys and frequent ANALYZE/VACUUM to keep query plans accurate, slowing streaming pipelines.

What syntax differences matter for developers?

ClickHouse favors SQL-92 with extensions: ENGINE clause on CREATE TABLE, functions like quantile(), and ARRAY joins. Redshift mirrors PostgreSQL but omits window functions such as FILTER and distinct aggregation extensions.

ClickHouse CREATE TABLE example

CREATE TABLE Orders
(
id UInt64,
customer_id UInt64,
order_date DateTime,
total_amount Decimal(10,2)
) ENGINE = MergeTree
ORDER BY (order_date, id);


Redshift equivalent

CREATE TABLE Orders (
id BIGINT,
customer_id BIGINT,
order_date TIMESTAMP,
total_amount DECIMAL(10,2)
)
DISTSTYLE KEY
DISTKEY (customer_id)
SORTKEY (order_date);


When should I still use Redshift?

Pick Redshift when you rely on AWS glue, Lake Formation, or Spectrum, need ANSI SQL parity for report builders, or prefer fully managed snapshots with cross-region replication.

Best practices for migrating to ClickHouse

Partition by event time; order by high-cardinality columns queried in ranges. Use MATERIALIZED VIEWs to pre-aggregate. Keep MergeTree parts under 300 MB to avoid large merges.

Common mistakes and fixes

Ignoring ORDER BY: Always set ORDER BY to match your most frequent filters; otherwise scans are full-table.

Over-sharding early: Start with single-replica clusters and add shards only when CPU >70% and merges lag.