Once data is flowing into Materialize and you start modeling it in SQL, you might run into some snags or unexpected scenarios. This guide collects common questions around data transformation to help you troubleshoot your queries.

Why is my query slow?

The most common reasons for query execution taking longer than expected are:

  • Processing lag in upstream dependencies, like materialized views and indexes
  • Index design
  • Query design

Each of these reasons requires a different approach for troubleshooting. Follow the guidance below to first detect the source of slowness, and then address it accordingly.

Lagging materialized views or indexes


When a materialized view or index upstream of your query is behind on processing, your query must wait for it to catch up before returning results. This is how Materialize ensures consistent results for all queries.

To check if any materialized views or indexes are lagging, use the workflow graphs in the Materialize console.

  1. Go to
  2. Click on the “Clusters” tab in the side navigation bar.
  3. Click on the cluster that contains your upstream materialized view or index.
  4. Go to the “Materialized Views” or “Indexes” section, and click on the object name to access its workflow graph.

If you find that one of the upstream materialized views or indexes is lagging, this could be the cause of your query slowness.


To troubleshoot and fix a lagging materialized view or index, follow the steps in the dataflow troubleshooting guide.

Do you have multiple materialized views chained on top of each other? Are you seeing small amounts of lag?
Tip: avoid intermediary materialized views where not necessary. Each chained materialized view incurs a small amount of processing lag from the previous one.

Other options to consider:

  • If you’ve gone through the dataflow troubleshooting and do not want to make any changes to your query, consider sizing up your cluster.
  • You can also consider changing your isolation level, depending on the consistency guarantees that you need. With a lower isolation level, you may be able to query stale results out of lagging indexes and materialized views.
  • You can also check whether you’re using a transaction and follow the guidance there.

Slow query execution

Query execution time largely depends on the amount of on-the-fly work that needs to be done to compute the result. You can cut back on execution time in a few ways:

Indexing and query optimization

Like in any other database, index design affects query performance. If the dependencies of your query don’t have indexes defined, you should consider creating one (or many). Check out the optimization guide for guidance on how to optimize query performance. For information on when to use a materialized view versus an index, check out the materialized view reference documentation .

If the dependencies of your query are indexed, you should confirm that the query is actually using the index! This information is available in the query plan, which you can view using the EXPLAIN PLAN command. If you run EXPLAIN PLAN for your query and see the index(es) under Used indexes, this means that the index was correctly used. If that’s not the case, consider:

  • Are you running the query in the same cluster which contains the index? You must do so in order for the index to be used.
  • Does the index’s indexed expression (key) match up with how you’re querying the data?

Result filtering

If you are just looking to validate data and don’t want to deal with query optimization at this stage, you can improve the efficiency of validation queries by reducing the amount of data that Materialize needs to read. You can achieve this by adding LIMIT clauses or temporal filters to your queries.

LIMIT clause

Use the standard LIMIT clause to return at most the specified number of rows. It’s important to note that this only applies to basic queries against a single source, materialized view or table, with no ordering, filters or offsets.

SELECT <column list or *>
FROM <source, materialized view or table>
LIMIT <25 or less>;

To verify whether the query will return quickly, use EXPLAIN PLAN to get the execution plan for the query, and validate that it starts with Explained Query (fast path).

Temporal filters

Use temporal flters to filter results on a timestamp column that correlates with the insertion or update time of each row. For example:

WHERE mz_now() <= event_ts + INTERVAL '1hr'

Materialize is able to “push down” temporal filters all the way down to its storage layer, skipping over old data that isn’t relevant to the query. For more details on temporal filter pushdown, see the reference documentation.

Other things to consider


Transactions are a database concept for bundling multiple query steps into a single, all-or-nothing operation. You can read more about them in the transactions section of our docs.

In Materialize, BEGIN starts a transaction block. All statements in a transaction block will be executed in a single transaction until an explicit COMMIT or ROLLBACK is given. All statements in that transaction happen at the same timestamp, and that timestamp must be valid for all objects the transaction may access.

What this means for latency: Materialize may delay queries against “slow” tables, materialized views, and indexes until they catch up to faster ones in the same schema. We recommend you avoid using transactions in contexts where you require low latency responses and are not certain that all objects in a schema will be equally current.

What you can do:

  • Avoid using transactions where you don’t need them. For example, if you’re only executing single statements at a time.
  • Double check whether your SQL library or ORM is wrapping all queries in transactions on your behalf, and disable that setting, only using transactions explicitly when you want them.

Client-side latency

To minimize the roundtrip latency associated with making requests from your client to Materialize, make your requests as physically close to your Materialize region as possible. For example, if you use the AWS us-east-1 region for Materialize, your client server would ideally also be running in AWS us-east-1.

Result size

Smaller results lead to less time spent transmitting data over the network. You can calculate your result size as number of rows returned x byte size of each row, where byte size of each row = sum(byte size of each column). If your result size is large, this will be a factor in query latency.

Cluster CPU

Another thing to check is how busy the cluster you’re issuing queries on is. A busy cluster means your query might be blocked by some other processing going on, taking longer to return. As an example, if you issue a lot of resource-intensive queries at once, that might spike the CPU.

The measure of cluster busyness is CPU. You can monitor CPU usage in the Materialize console by clicking the “Clusters” tab in the navigation bar, and clicking into the cluster. You can also grab CPU usage from the system catalog using SQL:

SELECT cru.cpu_percent
FROM mz_internal.mz_cluster_replica_utilization cru
LEFT JOIN mz_catalog.mz_cluster_replicas cr ON cru.replica_id =
LEFT JOIN mz_catalog.mz_clusters c ON cr.cluster_id =

Why is my query not responding?

The most common reasons for query hanging are:

  • An upstream source is stalled
  • Your cluster is unhealthy

Each of these reasons requires a different approach for troubleshooting. Follow the guidance below to first detect the source of the hang, and then address it accordingly.

NOTE: Your query may be running, just slowly. If none of the reasons below detects your issue, jump to Why is my query slow? for further guidance.

Stalled source

To detect and address stalled sources, follow the Ingest data troubleshooting guide.

Unhealthy cluster


If your cluster runs out of memory (i.e., it OOMs), this will result in a crash. After a crash, the cluster has to restart, which can take a few seconds. On cluster restart, your query will also automatically restart execution from the beginning.

If your cluster is CPU-maxed out (~100% utilization), your query may be blocked while the cluster processes the other activity. It may eventually complete, but it will continue to be slow and potentially blocked until the CPU usage goes down. As an example, if you issue a lot of resource-intensive queries at once, that might spike the CPU.

To see memory and CPU usage for your cluster in the Materialize console, go to, click the “Clusters” tab in the navigation bar, and click on the cluster name.


Your query may have been the root cause of the increased memory and CPU usage, or it may have been something else happening on the cluster at the same time. To troubleshoot and fix memory and CPU usage, follow the steps in the dataflow troubleshooting guide.

For guidance on how to reduce memory and CPU usage for this or another query, take a look at the indexing and query optimization and result filtering sections above.

If your query was the root cause, you’ll need to kill it for the cluster’s memory or CPU to go down. If your query was causing an OOM, the cluster will continue to be in an “OOM loop” - every time the cluster restarts, the query restarts executing automatically then causes an OOM again - until you kill the query.

If your query was not the root cause, you can wait for the other activity on the cluster to stop and memory/CPU to go down, or switch to a different cluster.

If you’ve gone through the dataflow troubleshooting and do not want to make any changes to your query, consider sizing up your cluster. A larger size cluster will provision more memory and CPU resources.

How do I troubleshoot slow queries?

Materialize stores a (sampled) log of the SQL statements that are issued against your Materialize region in the last three days, along with various metadata about these statements. You can access this log via the “Query history” tab in the Materialize console. You can filter and sort statements by type, duration, and other dimensions.

This data is also available via the mz_internal.mz_recent_activity_log catalog table.

It’s important to note that the default (and max) sample rate for most Materialize organizations is 99%, which means that not all statements will be captured in the log. The sampling rate is not user-configurable, and may change at any time.

If you’re looking for a complete audit history, use the mz_audit_events catalog table, which records all DDL commands issued against your Materialize region.

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