Troubleshooting
This page describes several SQL queries you can run to diagnose performance issues with Materialize.
Mental model and basic terminology
When you create a materialized view, an index, or issue an ad-hoc query, Materialize creates a so-called dataflow. A dataflow consists of instructions on how to respond to data input and to changes to that data. Once executed, the dataflow computes the result of the SQL query, waits for updates from the sources, and then incrementally updates the query results when new data arrives.
Materialize dataflows act on collections of data. To provide fast access to the changes to individual records, the records can be stored in an indexed representation called arrangements. Arrangements can be manually created by users on views by creating an index on the view. But they are also used internally in dataflows, for instance, when joining relations.
Translating SQL to dataflows
To make these concepts a bit more tangible, let’s look at the example from the getting started guide.
CREATE SOURCE auction_house
FROM LOAD GENERATOR AUCTION
(TICK INTERVAL '100ms')
FOR ALL TABLES
WITH (SIZE = '3xsmall');
CREATE MATERIALIZED VIEW num_bids AS
SELECT auctions.item, count(bids.id) AS number_of_bids
FROM bids
JOIN auctions ON bids.auction_id = auctions.id
WHERE bids.bid_time < auctions.end_time
GROUP BY auctions.item;
CREATE INDEX num_bids_idx ON num_bids (item);
The query of the materialized view joins the relations bids and auctions, groups by auctions.item and determines the number of bids per auction. To understand how this SQL query is translated to a dataflow, we can use EXPLAIN PLAN to display the plan used to evaluate the join.
EXPLAIN MATERIALIZED VIEW num_bids
Optimized Plan
-----------------------------------------------
materialize.qck.num_bids: +
Reduce group_by=[#0] aggregates=[count(*)] +
Project (#3) +
Filter (#1 < #4) +
Join on=(#0 = #2) type=differential +
ArrangeBy keys=[[#0]] +
Project (#2, #4) +
Get materialize.qck.bids +
ArrangeBy keys=[[#0]] +
Project (#0, #2, #3) +
Get materialize.qck.auctions +
(1 row)
The plan describes the specific operators that are used to evaluate the query.
Some of these operators resemble relational algebra or map reduce style operators (Filter, Join, Project).
Others are specific to Materialize (Get, ArrangeBy).
In general, a high level understanding of what these operators do is sufficient for effective debugging:
Filter filters records, Join joins records from two or more inputs, Map applies a function to transform records, etc.
You can find more details on these operators in the EXPLAIN PLAN documentation.
But it’s not important to have a deep understanding of all these operators for effective debugging.
Behind the scenes, the operator graph is turned into a dataflow.
The dataflow is organized in a hierarchical structure that contains operators and regions, which define a hierarchy on the operators.
In our example, the dataflow contains an InputRegion, a BuildRegion, and a region for the sink.
Again, it’s not too important for our purposes to understand what these regions do and how they are used to structure the operator graph. For our purposes it’s just important to know than that they define a hierarchy on the operators.
The system catalog and introspection relations
Materialize collects a lot of useful information about the dataflows and operators in the system catalog in introspection relations. The introspection relations are useful to troubleshoot and understand what is happening under the hood when Materialize is not behaving as expected. However, it is important to understand that most of the statistics we need for troubleshooting purposes are specific to the cluster that is running the queries we want to debug.
cluster and cluster_replica session variables.
Where is Materialize spending compute time?
Materialize spends time in various dataflow operators. Dataflows are created to maintain materialized views or indexes. In addition, temporary dataflow will be created for ad-hoc queries that don’t just make a lookup to an existing index.
If Materialize is taking more time to update results than you expect, you can identify which operators take the largest total amount of time.
Identifying expensive dataflows
To understand which dataflow is taking the most time we can query the mz_scheduling_elapsed relation.
The elapsed_time metric shows the absolute time the dataflows was busy since the system started and the dataflow was created.
-- Extract raw elapsed time information for dataflows
SELECT
mdo.id,
mdo.name,
mse.elapsed_ns / 1000 * '1 MICROSECONDS'::interval AS elapsed_time
FROM mz_internal.mz_scheduling_elapsed AS mse,
mz_internal.mz_dataflow_operators AS mdo,
mz_internal.mz_dataflow_addresses AS mda
WHERE mse.id = mdo.id AND mdo.id = mda.id AND list_length(address) = 1
ORDER BY elapsed_ns DESC
id | name | elapsed_time
-----+----------------------------------------+-----------------
354 | Dataflow: materialize.qck.num_bids | 02:05:25.756836
578 | Dataflow: materialize.qck.num_bids_idx | 00:15:04.838741
(2 rows)
These results show that Materialize spends the most time keeping the materialized view num_bids up to date,
followed by the work on the index avg_bids_idx.
Identifying expensive operators in a dataflow
The previous query is a good starting point to get an overview of the work happening in the cluster because it only returns dataflows.
The mz_scheduling_elapsed relation also contains details for regions and operators.
Removing the condition list_length(address) = 1 will include the regions and operators in the result.
But be aware that every row shows aggregated times for all the elements it contains.
The elapsed_time reported for the dataflows above also includes the elapsed time for all the regions and operators they contain.
But because the parent-child relationship is not always obvious,
the results containing a mixture of dataflows, regions, and operators can be a bit hard to interpret.
The following query therefore only returns operators from the mz_scheduling_elapsed relation.
You can further drill down by adding a filter condition that matches the name of a specific dataflow.
SELECT
mdod.id,
mdod.name,
mdod.dataflow_name,
mse.elapsed_ns / 1000 * '1 MICROSECONDS'::interval AS elapsed_time
FROM mz_internal.mz_scheduling_elapsed AS mse,
mz_internal.mz_dataflow_addresses AS mda,
mz_internal.mz_dataflow_operator_dataflows AS mdod
WHERE
mse.id = mdod.id AND mdod.id = mda.id
-- exclude regions and just return operators
AND mda.address NOT IN (
SELECT DISTINCT address[:list_length(address) - 1]
FROM mz_internal.mz_dataflow_addresses
)
ORDER BY elapsed_ns DESC
id | name | dataflow_name | elapsed_time
-----+-------------------------------------------------+----------------------------------------+-----------------
431 | ArrangeBy[[Column(0)]] | Dataflow: materialize.qck.num_bids | 01:12:58.964875
442 | ArrangeBy[[Column(0)]] | Dataflow: materialize.qck.num_bids | 00:06:06.080178
528 | shard_source_fetch(u517) | Dataflow: materialize.qck.num_bids | 00:04:04.076344
594 | shard_source_fetch(u517) | Dataflow: materialize.qck.num_bids_idx | 00:03:34.803234
590 | persist_source_backpressure(backpressure(u517)) | Dataflow: materialize.qck.num_bids_idx | 00:03:33.626036
400 | persist_source_backpressure(backpressure(u510)) | Dataflow: materialize.qck.num_bids | 00:03:03.575832
...
From the results of this query we can see that most of the elapsed time of the dataflow is caused by the time it takes to maintain the updates in one of the arrangements of the dataflow of the materialized view.
Why is Materialize unresponsive or slow?
A large class of problems can be identified by using elapsed_time to estimate the most expensive dataflows and operators.
However, elapsed_time contains all work since the operator or dataflow was first created.
Sometimes, a lot of work happens initially when the operator is created, but later on it takes only little continuous effort.
If you want to see what operator is taking the most time right now,
the elapsed_time metric is not enough.
The relation mz_compute_operator_durations_histogram also tracks the time operators are busy,
but instead of aggregating elapsed_time since an operator got created,
it tracks how long each operator was scheduled at a time in a histogram.
This information can show you two things: operators that block progress for others and operators that are currently doing work.
If there is a very expensive operator that blocks progress for all other operators, it will become visible in the histogram. The offending operator will be scheduled in much longer intervals compared to other operators, which reflects in the histogram as larger time buckets.
-- Extract raw scheduling histogram information for operators
WITH histograms AS (
SELECT
mdod.id,
mdod.name,
mdod.dataflow_name,
mcodh.count,
mcodh.duration_ns / 1000 * '1 MICROSECONDS'::interval AS duration
FROM mz_internal.mz_compute_operator_durations_histogram AS mcodh,
mz_internal.mz_dataflow_addresses AS mda,
mz_internal.mz_dataflow_operator_dataflows AS mdod
WHERE
mcodh.id = mdod.id
AND mdod.id = mda.id
-- exclude regions and just return operators
AND mda.address NOT IN (
SELECT DISTINCT address[:list_length(address) - 1]
FROM mz_internal.mz_dataflow_addresses
)
)
SELECT *
FROM histograms
WHERE duration > '100 millisecond'::interval
ORDER BY duration DESC
id | name | dataflow_name | count | duration
-----+--------------------------------------+----------------------------------------+-------+-----------------
408 | persist_source::decode_and_mfp(u510) | Dataflow: materialize.qck.num_bids | 1 | 00:00:01.073741
408 | persist_source::decode_and_mfp(u510) | Dataflow: materialize.qck.num_bids | 1 | 00:00:00.53687
374 | persist_source::decode_and_mfp(u509) | Dataflow: materialize.qck.num_bids | 1 | 00:00:00.268435
408 | persist_source::decode_and_mfp(u510) | Dataflow: materialize.qck.num_bids | 2 | 00:00:00.268435
429 | FormArrangementKey | Dataflow: materialize.qck.num_bids | 2 | 00:00:00.134217
(5 rows)
Note that this relation contains a lot of information.
The query therefore filters all durations below 100 millisecond.
In this example, the result of the query shows that the longest an operator got scheduled is just over one second.
So it’s unlikely that Materialize is unresponsive because of a single operator blocking progress for others.
The reported duration is still reporting aggregated values since the operator has been created. To get a feeling for which operators are currently doing work, you can subscribe to the changes of the relation.
-- Observe changes to the raw scheduling histogram information
COPY(SUBSCRIBE(
WITH histograms AS (
SELECT
mdod.id,
mdod.name,
mdod.dataflow_name,
mcodh.count,
mcodh.duration_ns / 1000 * '1 MICROSECONDS'::interval AS duration
FROM mz_internal.mz_compute_operator_durations_histogram AS mcodh,
mz_internal.mz_dataflow_addresses AS mda,
mz_internal.mz_dataflow_operator_dataflows AS mdod
WHERE
mcodh.id = mdod.id
AND mdod.id = mda.id
-- exclude regions and just return operators
AND mda.address NOT IN (
SELECT DISTINCT address[:list_length(address) - 1]
FROM mz_internal.mz_dataflow_addresses
)
)
SELECT *
FROM histograms
WHERE duration > '100 millisecond'::interval
) WITH (SNAPSHOT = false, PROGRESS)) TO STDOUT
1691667343000 t \N \N \N \N \N \N
1691667344000 t \N \N \N \N \N \N
1691667344000 f -1 431 ArrangeBy[[Column(0)]] Dataflow: materialize.qck.num_bids 7673 00:00:00.104800
1691667344000 f 1 431 ArrangeBy[[Column(0)]] Dataflow: materialize.qck.num_bids 7674 00:00:00.104800
1691667345000 t \N \N \N \N \N \N
...
In this way you can see that currently the only operator that is doing more than 100 milliseconds worth of work
is the ArrangeBy operator from the materialized view num_bids.
Why is Materialize using so much memory?
Arrangements take up most of Materialize’s memory use. Arrangements maintain indexes for data as it changes. These queries extract the numbers of records and the size of the arrangements. The reported records may exceed the number of logical records; the report reflects the uncompacted state.
-- Extract dataflow records and sizes
SELECT
id,
name,
records,
round(size / 1024 / 1024, 2) AS size_mb
FROM mz_internal.mz_dataflow_arrangement_sizes
ORDER BY size DESC
id | name | records | size_mb
-------+----------------------------------------+---------+---------
19157 | Dataflow: materialize.qck.num_bids | 1612747 | 113.82
19158 | Dataflow: materialize.qck.num_bids_idx | 13 | 0
If you need to drill down into individual operators, you can query mz_arrangement_sizes instead.
-- Extract operator records and sizes
SELECT
mdod.id,
mdod.name,
mdod.dataflow_name,
mas.records,
round(size / 1024 / 1024, 2) AS size_mb
FROM mz_internal.mz_arrangement_sizes AS mas,
mz_internal.mz_dataflow_operator_dataflows AS mdod
WHERE mas.operator_id = mdod.id
ORDER BY mas.records DESC
id | name | dataflow_name | records | size_mb
---------+-------------------------------+----------------------------------------+---------+---------
2722012 | ArrangeBy[[Column(0)]] | Dataflow: materialize.qck.num_bids | 1612747 | 113.82
2722027 | ArrangeBy[[Column(0)]] | Dataflow: materialize.qck.num_bids | 292662 | 20.65
2722216 | ArrangeBy[[Column(0)]] | Dataflow: materialize.qck.num_bids_idx | 17 | 0
2722077 | ReduceAccumulable | Dataflow: materialize.qck.num_bids | 5 | 0
2722073 | ArrangeAccumulable | Dataflow: materialize.qck.num_bids | 5 | 0
2722081 | AccumulableErrorCheck | Dataflow: materialize.qck.num_bids | 0 | 0
2722225 | ArrangeBy[[Column(0)]]-errors | Dataflow: materialize.qck.num_bids_idx | 0 | 0
(7 rows)
We’ve also bundled an interactive, web-based memory usage visualization tool to aid in debugging. The memory visualization tool shows all user-created arrangements, grouped by dataflow. The amount of memory used by Materialize should correlate with the number of arrangement records that are displayed by either the visual interface or the SQL queries.
You can access the memory usage visualization for your Materialize region at
https://<region host>/memory.
Is work distributed equally across workers?
Work is distributed across workers by the hash of their keys. Thus, work can become skewed if situations arise where Materialize needs to use arrangements with very few or no keys. Example situations include:
- Views that maintain order by/limit/offset
- Cross joins
- Joins where the join columns have very few unique values
Additionally, the operators that implement data sources may demonstrate skew, as they (currently) have a granularity determined by the source itself. For example, Kafka topic ingestion work can become skewed if most of the data is in only one out of multiple partitions.
-- Get operators where one worker has spent more than 2 times the average
-- amount of time spent. The number 2 can be changed according to the threshold
-- for the amount of skew deemed problematic.
SELECT
mse.id,
dod.name,
mse.worker_id,
elapsed_ns,
avg_ns,
elapsed_ns/avg_ns AS ratio
FROM
mz_internal.mz_scheduling_elapsed_per_worker mse,
(
SELECT
id,
avg(elapsed_ns) AS avg_ns
FROM
mz_internal.mz_scheduling_elapsed_per_worker
GROUP BY
id
) aebi,
mz_internal.mz_dataflow_operator_dataflows dod
WHERE
mse.id = aebi.id AND
mse.elapsed_ns > 2 * aebi.avg_ns AND
mse.id = dod.id
ORDER BY ratio DESC;
I found a problematic operator. Where did it come from?
Look up the operator in mz_dataflow_addresses. If an operator has
value x at position n, then it is part of the x subregion of the region
defined by positions 0..n-1. The example SQL query and result below shows an
operator whose id is 515 that belongs to “subregion 5 of region 1 of dataflow
21”.
SELECT * FROM mz_internal.mz_dataflow_addresses WHERE id=515;
id | worker_id | address
-----+-----------+----------
515 | 0 | {21,1,5}
Usually, it is only important to know the name of the dataflow a problematic operator comes from. Once the name is known, the dataflow can be correlated to an index or materialized view in Materialize.
Each dataflow has an operator representing the entire dataflow. The address of said operator has only a single entry. For the example operator 515 above, you can find the name of the dataflow if you can find the name of the operator whose address is just “dataflow 21.”
-- get id and name of the operator representing the entirety of the dataflow
-- that a problematic operator comes from
SELECT
mdo.id AS id,
mdo.name AS name
FROM
mz_internal.mz_dataflow_addresses mda,
-- source of operator names
mz_internal.mz_dataflow_operators mdo,
-- view containing operators representing entire dataflows
(SELECT
mda.id AS dataflow_operator,
mda.address[1] AS dataflow_address
FROM
mz_internal.mz_dataflow_addresses mda
WHERE
list_length(mda.address) = 1) dataflows
WHERE
mda.id = 515
AND mda.address[1] = dataflows.dataflow_address
AND mdo.id = dataflows.dataflow_operator;
Why isn’t my source ingesting data?
First, look for errors in mz_source_statuses:
SELECT * FROM mz_internal.mz_source_statuses
WHERE name = <SOURCE_NAME>;
If your source reports a status of stalled or failed, you likely have a
configuration issue. The returned error field will provide details.
If your source reports a status of starting for more than a few minutes,
contact support.
If your source reports a status of running, but you are not receiving data
when you query the source, the source may still be ingesting its initial
snapshot. See the next section.
Has my source ingested its initial snapshot?
Query the snapshot_comitted field of the
mz_source_statistics table:
SELECT bool_and(snapshot_committed) as snapshot_committed
FROM mz_internal.mz_source_statistics
WHERE id = <SOURCE ID>;
You generally want to aggregate the snapshot_committed field across all worker
threads, as done in the above query. The snapshot is only considered committed
for the source as a whole once all worker threads have committed their
components of the snapshot.
Even if your source has not yet committed its initial snapshot, you can still monitor its progress. See the next section.
How do I monitor source ingestion progress?
Repeatedly query the
mz_source_statistics
table and look for ingestion statistics that advance over time:
SELECT
SUM(bytes_received) AS bytes_received,
SUM(messages_received) AS messages_received,
SUM(updates_staged) AS updates_staged,
SUM(updates_committed) AS updates_committed
FROM mz_internal.mz_source_statistics
WHERE id = <SOURCE ID>;
(You can also look at statistics for individual worker threads to evaluate whether ingestion progress is skewed, but it’s generally simplest to start by looking at the aggregate statistics for the whole source.)
The bytes_received and messages_received statistics should roughly
correspond with the external system’s measure of progress. For example, the
bytes_received and messages_received fields for a Kafka source should
roughly correspond to what the upstream Kafka broker reports as the number of
bytes (including the key) and number of messages transmitted, respectively.
During the initial snapshot, updates_committed will remain at zero until all
messages in the snapshot have been staged. Only then will updates_committed
advance. This is expected, and not a cause for concern.
After the initial snapshot, there should be relatively little skew between
updates_staged and updates_committed. A large gap is usually an indication
that the source has fallen behind, and that you likely need to scale up your
source.
messages_received does not necessarily correspond with updates_staged
and updates_commmited. For example, an UPSERT envelope can have more
updates than messages, because messages can cause both deletions and insertions
(i.e. when they update a value for a key), which are both counted in the
updates_* statistics. There can also be fewer updates than messages, as
many messages for a single key can be consolidated if they occur within a (small)
internally configured window. That said, messages_received making
steady progress, while updates_staged/updates_committed doesn’t is also
evidence that a source has fallen behind, and may need to be scaled up.
Beware that these statistics periodically reset to zero, as internal components of the system restart. This is expected behavior. As a result, you should restrict your attention to how these statistics evolve over time, and not their absolute values at any moment in time.
Why isn’t my sink exporting data?
First, look for errors in mz_sink_statuses:
SELECT * FROM mz_internal.mz_sink_statuses
WHERE name = <SINK_NAME>;
If your sink reports a status of stalled or failed, you likely have a
configuration issue. The returned error field will provide details.
If your sink reports a status of starting for more than a few minutes,
contact support.
How do I monitor sink ingestion progress?
Repeatedly query the
mz_sink_statistics
table and look for ingestion statistics that advance over time:
SELECT
SUM(messages_staged) AS messages_staged,
SUM(messages_committed) AS messages_committed,
SUM(bytes_staged) AS bytes_staged,
SUM(bytes_committed) AS bytes_committed
FROM mz_internal.mz_sink_statistics
WHERE id = <SINK ID>;
(You can also look at statistics for individual worker threads to evaluate whether ingestion progress is skewed, but it’s generally simplest to start by looking at the aggregate statistics for the whole source.)
The messages_staged and bytes_staged statistics should roughly correspond
with what materialize has written (but not necessarily committed) to the
external service. For example, the bytes_staged and messages_staged fields
for a Kafka sink should roughly correspond with how many messages materialize
has written to the Kafka topic, and how big they are (including the key), but
the Kafka transaction for those messages might not have been committed yet.
messages_committed and bytes_committed correspond to the number of messages
committed to the external service. These numbers can be smaller than the
*_staged statistics, because Materialize might fail to write transactions and
retry them.
If any of these statistics are not making progress, your sink might be stalled or need to be scaled up.
If the *_staged statistics are making progress, but the *_committed ones
are not, there may be a configuration issues with the external service that is
preventing Materialize from committing transactions. Check the reason
column in mz_sink_statuses, which can provide more information.