Why JOINs in ClickHouse cost more and when to remove them
JOINs in ClickHouse are inherently more expensive than querying one denormalized table, so the goal is to move the attributes your dashboards need out of the query path and into the table that is scanned. Picture a team whose dashboards slowed as a multi-billion-row event table was joined to customer, product and region dimensions on every refresh. Each query paid for the join again, and the cost grew with the event volume rather than with the size of the answer. ClickHouse's JOIN best-practice guidance recommends denormalizing frequently queried attributes into the fact table and using incremental materialized views to precompute aggregates at insert time, so dashboards read a small, already-shaped result instead of rebuilding it from raw events. Start by listing the dimension columns that appear in most dashboard filters and groupings, since those are the candidates to copy into the wide table. The guidance is available in the ClickHouse JOIN best practices.
Denormalizing is not free. Copying attributes into the event table means that when a dimension value changes, such as a customer moving region, the historical rows either keep the old value or need a correction. The team must decide which behavior the business expects and confirm how corrections will be applied to existing data, before the first backfill. Writing the schema change, the backfill and the insert path for a live cluster is design work that depends on the table engines, ordering keys, data volumes and ingestion pattern already in place, so none of these steps should be treated as a one-line change. DeployOpen plans and builds these schema changes inside your own infrastructure, including the materialized views, backfill sequence and a test on a copy of production data. Confirm with your own team which dimensions change often and which queries are truly latency sensitive before scoping the work.
Not every join should be removed. Small, slowly changing lookups are often better served by dictionaries, which keep reference data in memory, and some relationships are simply too large or too volatile to copy. Ask for a written inventory of every join in the slowest dashboards, with the table sizes, the join keys and the freshness each consumer needs. That inventory decides which joins become denormalized columns, which become dictionary lookups, which become materialized views and which stay as joins. Those choices also determine how much the insert path and storage grow, so record them alongside the rollout plan and test the slowest queries again on the changed schema.
Which join fix fits which query pattern
A small reference table such as a product catalog, region list or account tier changes rarely, and most queries only need a few of its attributes. The usual preferred fix is to denormalize those attributes into the fact table at insert time, so queries read one wide table and skip the join entirely. When the attribute set is large or changes often, a dictionary is the next option, because it serves lookups from memory without a join in the query plan.
- Denormalize at insert: Copy the frequently queried attributes into the fact table during ingestion, so each row carries the values it needs.
- Dictionary for enrichment: Load the dimension into a dictionary and call
dictGetin queries or at insert time, which avoids a join operator. - Trade-off on staleness: Denormalized values are fixed at the time the row is written, so a renamed product or changed region will not update old rows without a backfill.
- Trade-off on memory: Dictionaries hold their data in server memory, so size the dimension and its refresh lifetime before loading it.
- Trade-off on schema change: Adding a new attribute to the wide table means a schema change and a decision about whether to backfill history.
Check how often the dimension changes and whether historical rows must reflect the value at the time of the event or the current value.
Joining two large event tables, such as orders to shipments or sessions to payments, is where join cost grows fastest. Denormalizing both sides into one table is often impractical because each side is written at a different time and by a different producer. In this case, the usual approach is to keep the join but tune it: choose the smaller table as the right side, filter both sides before the join, and make sure the join keys match the ordering keys so reads stay selective.
- Keep the join with tuned order: Put the smaller, filtered side on the right and filter by time range on both sides before joining.
- Align keys with ordering: Use join columns that appear early in the ordering key of each table, so ClickHouse reads fewer granules.
- Materialized view for a fixed shape: If the same join runs repeatedly with the same keys, a materialized view that writes the joined rows at insert time can replace it, but only when the join rules fit insert-time processing.
- Trade-off on insert cost: Insert-time joins add work to every write and can miss rows whose matching side arrives later.
- Trade-off on late data: Events that arrive out of order need a rule for when a match is considered final.
Test the query with EXPLAIN and compare rows read on a copy of the production data before changing the design.
Dashboards that recompute the same aggregate across joined tables on every refresh are a common source of cost. Re-running the join each time repeats work that rarely changes. The preferred fix is an incremental materialized view that computes the aggregate as data is inserted and stores the result in a target table, so dashboards read a small precomputed table. This moves cost from query time to insert time, which suits data that is append-only or where the aggregate is additive.
- Incremental materialized view: Compute the aggregate on each insert block and write it to a target table such as an
AggregatingMergeTreetable. - Dimension lookups at insert time: Enrich rows through a dictionary or denormalized columns before aggregation, so the view itself needs no join.
- Trade-off on insert cost: Each insert runs the view query, so write throughput falls as the number of views and their complexity grows.
- Trade-off on corrections: Updates to source rows do not automatically change aggregates already stored, so corrections need a rebuild or a backfill plan.
- Trade-off on non-additive metrics: Distinct counts and some percentiles need specific aggregate state functions, so confirm the metric can be stored incrementally.
For aggregates that cannot be built incrementally, a scheduled refresh may fit better, and that choice should be confirmed against the documentation for your version.
Some queries must reflect the latest state of a reference table, such as a customer's current risk tier or an account's active status. Denormalized copies and dictionaries refresh on a schedule, so they can lag behind the source. In these cases, keeping the join at query time is often the correct choice, with the reference table ordered and filtered so the join stays cheap. The trade-off is that query cost depends on the size of both sides and on how often dashboards run.
- Keep the join with a small right side: Limit the reference table to the rows and columns the query needs, and place it on the right side of the join.
- Dictionary with a short refresh interval: Use a dictionary when the lookup is read-heavy and a bounded delay is acceptable, and set its lifetime to match the freshness the business requires.
- Trade-off on freshness: A dictionary reflects the source only as of its last load, so state the maximum acceptable delay in writing.
- Trade-off on query cost: A query-time join repeats for each execution, so high-frequency dashboards amplify the cost.
- Trade-off on consistency: Values read from a dictionary and a join can differ within the same report if the refresh times differ.
Ask the data owner how stale a value may be before choosing between a join and a dictionary, and record that answer as part of the design.
Join optimization options and when each fits
This table compares four approaches to reducing join cost in self-managed ClickHouse: denormalized columns, dictionaries, incremental materialized views, and a retained JOIN with table order tuned. Read each row for what the option changes, the workload it suits, and the cost or risk to confirm before choosing. ClickHouse states that joins are inherently more expensive than querying one denormalized table, so the main takeaway is to remove joins from hot query paths where the workload allows it.
| Option | What it changes | Best fit | Cost or risk to confirm |
|---|---|---|---|
| Denormalized columns | Copies frequently queried attributes into the fact table at load time so queries read one table | Stable attributes read on most dashboard or log queries, such as a customer tier or region | Ingest cost and storage for duplicated values, plus the process to backfill and correct them when source attributes change |
| Dictionaries | Serves small-to-medium lookup data from memory in place of a join at query or insert time | Key-to-value enrichment where the lookup table changes slowly and fits in memory | Memory use, refresh lifetime and staleness; see the dictionaries topic for layouts and sources |
| Incremental materialized views | Precomputes joined or aggregated results as rows arrive, writing them to a target table | Recurring aggregates over a large fact table that dashboards query repeatedly | Extra insert work per view, backfill of historical data, and handling of late or updated source rows; confirm the join logic before relying on it |
| Retained JOIN with table order tuned | Keeps the normalized model and runs the join at query time, with the smaller table placed on the right side as the build input | Ad hoc analysis, rarely run queries, or lookups too large to denormalize cheaply | Query latency and memory grow with table size; test with representative data and check row counts with EXPLAIN and system.query_log |
How to plan a JOIN reduction in a self-managed cluster
This sequence turns a list of slow joined queries into tested, backfilled schema changes that dashboards use, with measured proof that rows read and latency fell. Each step closes with a check so the team knows when to move on.
Capture the slowest joined queries
Query
system.query_logfor recent queries that contain a JOIN, filtered by the users and dashboards that matter most, and sort them by total duration and read rows. Record the query text, the tables involved, and how often each pattern runs, because a join that runs thousands of times a day usually matters more than one slow ad hoc query. The step is complete when you hold a ranked list of distinct join patterns with baseline numbers for duration, rows read and run count.Classify each join by the attribute it needs
For each pattern, identify which columns the query takes from the right-hand table and whether they are stable attributes, such as a customer name or product category, or values that change with every event. Also note whether the join filters rows or only enriches them. Stable, low-cardinality attributes are candidates for denormalization or dictionaries, while fast-changing or aggregated values usually point to a materialized view. The step is complete when every pattern has one attribute class and one tentative fix written beside it.
Choose a fix for each case
Assign each pattern one of three approaches: denormalize frequently queried attributes into the fact table, load the lookup side into a dictionary for key-based enrichment, or precompute the result with a materialized view when the query is an aggregate. Record the trade-off for each choice, including the insert-time cost of denormalized columns, the memory a dictionary uses, and the storage a view's target table consumes. ClickHouse documents that joins are inherently more expensive than querying one denormalized table, so the default should be the simplest option that removes the join from the hot path. The step is complete when the decision and its trade-off are written down for every pattern.
Build the change on a copy
Create the new table, dictionary or view on a copy of the cluster or a separate test cluster loaded with representative data, including the skew and volume of production. Confirm the team's own query texts run unchanged against the new objects, and check that the results match the original join output for a sample of keys and time ranges. Watch for stale enrichment, missing keys and duplicate rows in the lookup source. The step is complete when results match on the test copy for every pattern that is moving.
Backfill and keep new data flowing
Load historical rows into the denormalized table or the view's target, and set up the ongoing path so new inserts carry the same attributes or update the aggregate. Decide how late-arriving lookup changes will be handled, for example by reloading a dictionary on a schedule or by accepting that enrichment reflects the value at insert time. Run the backfill in a window that does not compete with dashboards, and confirm row counts match the source for each partition. The step is complete when historical and new data both reconcile with the source tables.
Switch dashboards and jobs to the new objects
Repoint dashboards, scheduled reports and application queries to the new table, dictionary function or view, one pattern at a time, keeping the original join query available until the cutover is confirmed. Check that access grants cover the new objects for every user and role that queried the old ones. Watch error logs and query results for a full business cycle before retiring the old path. The step is complete when all targeted queries run against the new objects and no traffic reaches the old join.
Verify rows read and latency against the baseline
Rerun the same captured queries and compare rows read, memory use and duration with the baseline from the first step, using
system.query_logfor both periods under comparable load. Confirm the results still match the original joins for production data, and record any pattern that did not improve, so it can return to the classification step. Document each change, its owner and the rollback path in the team's runbook. The step is complete when each pattern has a before and after measurement and a documented outcome.
Trade-offs that change the answer
Denormalizing and materializing JOIN work moves cost from query time to insert time, storage and maintenance. Each card below names one trade-off that can flip the decision, and the measurement that shows which side your workload lands on.
Stale dimension values
When you copy a customer or product attribute into a fact table, that copy is frozen at insert time. A renamed account or a reclassified product stays wrong in historical rows unless you rewrite them. Decide per attribute whether history should reflect the value at event time or the current value, then test a sample of changed dimension records against the query results your dashboards expect. Measure how often dimension attributes change and how far back reports must stay correct.
Backfill cost on existing data
An incremental materialized view only processes rows inserted after it is created. Existing tables need a separate backfill, which reads and rewrites history and can compete with live ingestion for CPU and disk. For a large event table, plan the backfill by partition, run it off-peak, and reconcile row counts between the source and the target before dashboards switch over. Ask who will run and verify the backfill, and how long it takes on a copy of your data.
Insert-time overhead
Every denormalizing step or view that joins or aggregates runs on each insert batch, so ingestion slows as the number of dependent views grows. The cost lands on the write path that agents and pipelines depend on, not on the analysts who asked for the faster query. Measure insert latency and parts created per second before and after adding each view, and confirm the ingestion path can absorb the peak batch rate with the new work included.
Schema duplication and update patterns
Denormalized columns repeat values across many rows, which increases storage and makes corrections more expensive. Updating a copied attribute means rewriting or mutating every affected row, while a normalized dimension changes in one place. Choose an update pattern before the schema is fixed, such as reloading a dimension table, using a versioned table, or accepting a delay. Measure the number of rows touched by a typical correction and how long that correction takes to become visible.
Memory use of dictionaries
Dictionaries replace many small lookups with an in-memory structure, but that structure is held in server memory for every node that loads it. Large key spaces, frequent refreshes and several dictionaries on the same server can push memory use above what the cluster was sized for. Check the dictionary size and memory use in system tables after a full load, confirm the refresh lifetime matches how often the source changes, and test memory headroom under concurrent queries.
Join behavior by table order and version
The join algorithm, the choice of which side builds the hash table, and whether a join is pushed down all depend on table order and the ClickHouse version. A query that is fast on one release can slow after an upgrade, and reordering tables can change the memory needed. Keep a fixed set of representative join queries, record rows read and peak memory for each, and rerun them on every version you plan to run in production before changing the query shape.
Questions teams ask before reducing JOIN cost
Should we denormalize our tables or use dictionaries to remove JOINs?
Denormalize attributes that your most frequent queries read together, and use dictionaries for small or slowly changing lookup tables that many queries share. ClickHouse documents that a JOIN is inherently more expensive than querying one denormalized table, so the first step is to find which joins run on every dashboard or report. Copying a stable attribute such as a product category into the fact table removes that join entirely, at the cost of storage and of a backfill when the attribute changes. Dictionaries keep the source of truth in one place and are refreshed on a lifetime you choose, which suits reference data more than high-churn facts. Most schemas end up using both, and the choice for each attribute should follow how often it changes and how often it is queried.
What drives the cost and effort of reducing JOIN cost in ClickHouse?
The main drivers are the number of joins in the slowest queries, the size of the tables on each side, how often the joined attributes change, and how many downstream dashboards, APIs or pipelines depend on the current query shape. Effort rises when fact tables are very large and must be backfilled, when reference data changes frequently, or when several teams share the same tables and must agree on the new schema. Costs fall when the query workload is concentrated on a few known patterns and the source data is clean. In a quote, ask which queries are in scope, how many tables change, whether backfills are included, who runs the cutover, and what is delivered as written documentation.
How long does it take to see results from JOIN optimization?
The time to measurable results depends on schema complexity and data volume, so no single duration applies. A targeted change, such as moving one dimension attribute into a fact table for one dashboard, can be verified in a short cycle once representative data is loaded. Work that touches a large fact table requires a backfill, reloading history into new tables and comparing results, which often takes longer than the schema change itself. Materialized views add a step where historical data must be backfilled before the view is trusted. Ask for a plan that separates the analysis, the schema change, the backfill and the verification, with a milestone for each, since those phases scale differently with data size.
What does our team need to provide before optimization work starts?
Three inputs make the work efficient. First, representative data: a copy or sample of the production tables that preserves row counts, key cardinality and skew, because join costs depend on those properties. Second, query logs: exports from system.query_log covering peak periods, showing the exact SQL, rows read, memory use and durations of the slowest JOIN queries. Third, a pre-production cluster running the same ClickHouse version as production, so that results and plan changes can be compared safely. The ClickHouse production guidance recommends a pre-production environment with representative data and queries. You will also need named owners for the dashboards and applications that use the affected queries, since they must test their changes.
Can our in-house team reduce JOIN cost without outside help?
Yes, if the team has ClickHouse operating experience, access to query logs and a safe place to test schema changes. Reading query plans, choosing between denormalization and dictionaries, and rewriting queries are learnable skills. Teams usually struggle with the backfill of large tables, coordinating changes across consumers and proving that results are unchanged after a rewrite. An outside partner is most useful when the number of affected queries is large, when production tables cannot be copied easily, or when the team lacks capacity to run a parallel pipeline during the cutover. Whichever route you take, agree on acceptance criteria before the first change.
How do we verify that a JOIN change is correct and faster?
Verify correctness first, then performance. Run the old and new queries against the same data snapshot and compare row counts, aggregates and sampled rows, treating any difference as a defect until it is explained. For performance, compare durations, rows read and memory use in system.query_log using the same parameters and concurrency as production. Check that a denormalized column stays in sync by testing inserts, updates and late-arriving records, since stale copies are the most common regression. Keep the previous schema available until the new one has run through at least one full reporting cycle. Documented acceptance criteria, a rollback step and a retained test script make the result auditable for your change board.
Does reducing JOIN cost change our security or compliance posture?
It can, because denormalization copies attributes into more tables, and each copy may hold personal or regulated data that needs the same access rules, retention periods and erasure handling as the original. Row policies, roles and retention settings must be reviewed for every new table or materialized view. Dictionaries that load from external sources need credentials managed with the same care as other database connections. Ask for an inventory of each new table and dictionary, its data classification and its access grants, so that erasure and access reviews remain complete after the change.
What a DeployOpen engagement covers for JOIN work
A DeployOpen engagement for JOIN work starts with a written scope agreed before any change is made to your ClickHouse cluster. The scope usually begins with a query inventory: the dashboards, scheduled jobs and ad hoc queries that join tables, how often each runs, and which joined attributes change rarely enough to copy into a wider fact table. From that inventory we propose the schema and view changes, which may include denormalized columns, dictionaries for slowly changing lookups, or incremental materialized views for repeated aggregates. Each change is listed as a deliverable with its acceptance test, so you can see exactly what will and will not be altered before work starts.
Implementation happens on a copy of representative data and queries first. We compare query plans and rows read before and after each change, and we document the results alongside the rollback steps for the production cutover. Your team keeps custody of production data and makes the go or no-go decision at each gate. DeployOpen plans and implements the changes in your environment, and the handover package includes the schema definitions, view logic, test queries, and a runbook for the insert paths that feed denormalized columns. Ask any provider to confirm which environments they will access and how credentials are issued and revoked.
Ongoing tuning can be added as a retainer once the initial work is complete, for example to review new query patterns each quarter or to re-test joins after a ClickHouse version change. Pricing for the engagement and any retainer follows from the written scope, so ask for line items that separate the inventory, build, testing and documentation phases, and state which of them recur. Confirm with the vendor which ClickHouse release your cluster runs, since join behavior and defaults can change between versions, and reread the changelog before any upgrade that affects the queries in scope.
How DeployOpen scopes and supports ClickHouse tuning
DeployOpen approaches join optimization as a design question before it is a query question. We review which joins run on the dashboard and ingestion paths, then agree in writing which tables move to denormalized or materialized forms, which stay as joins, and how each change is verified on your own data. Engagements are fixed-scope, and ongoing support is available as a retainer.
How we scope
- Inventory the queries that join tables, with their frequency, latency targets and owners.
- Agree in writing which joins to denormalize, dictionary-enrich, convert to materialized views or leave unchanged.
- Confirm the representative data volume and the pre-production ClickHouse environment used for testing.
How we build
- Prototype each denormalized table or materialized view on a copy of production-shaped data first.
- Compare query plans and rows read before and after each change, using EXPLAIN and system.query_log.
- Document backfill steps for existing rows and the insert cost each new view adds to ingestion.
How we hand over
- Deliver schema definitions, view definitions, dictionary configuration and the join rules your team must follow.
- Record the before and after query benchmarks, with the test data and version used for each run.
- Walk your engineers through the change process for adding new attributes or joins later.
How we support
- Offer ongoing support and periodic query reviews as a retainer, scoped in writing.
- Review changelogs with your team before upgrades, since minor backward-incompatible changes can occur.
- Re-test join-heavy dashboards after schema changes or ClickHouse version updates on a pre-production cluster.
