ClickHouse vs PostgreSQL performance

Analytical aggregation is the shape ClickHouse is built for. It is an online analytical processing database with column-oriented storage, so a query that reads a few columns across millions or billions of rows touches only those columns on disk. ClickHouse documents that column storage speeds filtering and aggregation over many rows, which is the reason it is usually the stronger choice for these queries.

• Wide scans with few columns: a query such as a daily sum of one metric grouped by a dimension reads only the referenced columns.
• Group-by over large history: counts, averages and percentiles across long time ranges are the workload where column storage typically shows its advantage.
• Append-heavy event data: logs, clickstream and transaction events are written in bulk and read in aggregate, which suits a column store.
• PostgreSQL still fits small aggregates: when the table is modest and the query already uses good indexes, PostgreSQL may be fast enough and avoids a second database.
• Check with your own data: measure p95 latency for your three to five heaviest aggregations on representative row counts before deciding.

Ask your team which aggregation queries they run most often and how much history each one scans.

Dashboards combine many filters, grouping choices and refresh intervals, so the same table is queried in many shapes. ClickHouse handles this well when filters apply to columns in its sorting key and when dashboards aggregate rather than list individual rows. Filters on columns outside the key, or dashboards that return raw rows, can lose that advantage, so the table design matters as much as the engine.

• Filters on the sorting key: choose the ordering key from the columns dashboards filter on most often, such as tenant and date.
• Many concurrent viewers: a BI tool refreshing many panels at once adds concurrent queries, so test concurrency, not just a single query.
• Pre-aggregated views: materialized views can keep common rollups current, but they add storage and maintenance work to plan.
• Ad hoc filters on any column: when users filter on arbitrary columns, PostgreSQL indexes may be easier to predict than a column store's key design.
• Confirm the BI tool's query pattern: log the SQL a dashboard generates and replay it against both databases.

Ask the BI team for the ten most-viewed dashboards and the filters each one applies.

Fetching one order, account or customer by its key is the shape where PostgreSQL is usually the better fit. Its row-oriented storage and B-tree indexes are designed to locate and return single rows quickly. ClickHouse documents that retrieving single rows is slower in a column store, because a row's values are spread across column files, so point lookups are not its strength.

• Primary key reads: an application that reads one record per request should stay on a transactional database.
• Low-latency responses: user-facing requests with tight latency targets are better served by PostgreSQL's indexes.
• Referential integrity: foreign keys, constraints and transactional guarantees are core PostgreSQL features that the application may depend on.
• Connection handling: many short application connections are typically managed with a pooler such as PgBouncer on the PostgreSQL side.
• Verify the access pattern: count how many requests per second read single rows versus aggregate rows before moving any workload.

Ask application owners which endpoints fetch single records and whether they depend on constraints or transactions.

Updating individual rows often, such as changing an order status or a balance, is a poor match for a column-oriented engine. PostgreSQL updates rows in place within its transactional model and offers row-level locking. ClickHouse is designed for bulk inserts and merges of data parts, so high-frequency single-row changes need a different design, such as appending new versions and resolving them at query time.

• Status changes: an order moving through several states generates many small updates that suit PostgreSQL.
• Append-and-resolve patterns: writing a new version of each record and selecting the latest at query time can work in ClickHouse, but it adds query complexity.
• Batch corrections: periodic bulk corrections are less disruptive to a column store than constant row edits.
• Deletes and corrections: check how the team's correction and deletion requirements map to the engine's supported operations before committing.
• Confirm update rates: measure updates per second and the number of rows each update touches.

Ask the data team how often source records change after they are written and whether consumers need the old values.

Many teams run transactional and analytical work at the same time, and a single engine rarely fits both well. A common pattern keeps PostgreSQL as the system of record for transactions and copies events or history into ClickHouse for analytics. This split adds a data pipeline to operate, but it lets each engine handle the shape it is built for. Any copy process needs monitoring, backfill procedures and a plan for late or corrected records.

• System of record in PostgreSQL: keep the transactional source of truth where constraints and lookups already work.
• Analytics copy in ClickHouse: replicate or stream selected tables for aggregation and dashboards.
• Pipeline cost: the sync job, its failures and its schema changes become work the team owns.
• Operational separation: analytical scans should not compete with transactional traffic on the same server.
• Test the whole path: verify freshness, correctness of counts and recovery after a failed sync.

Ask the team which systems must read data within seconds and which can tolerate a delay of minutes or hours.