> ## Documentation Index
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> Use this file to discover all available pages before exploring further.

# Useful queries for troubleshooting

> A collection of handy queries for troubleshooting ClickHouse, including monitoring table sizes, long-running queries, and errors.

{frontMatter.description}

<h2 id="useful-queries-for-troubleshooting">
  Useful queries for troubleshooting
</h2>

In no particular order, here are some handy queries for troubleshooting ClickHouse and figuring out what is happening.

We also have a great blog with some [essential queries for monitoring ClickHouse](https://clickhouse.com/blog/monitoring-troubleshooting-select-queries-clickhouse).

<h2 id="view-which-settings-have-been-changed-from-the-default">
  View which settings have been changed from the default
</h2>

```sql theme={null}
SELECT
    name,
    value
FROM system.settings
WHERE changed
```

<h2 id="get-the-size-of-all-your-tables">
  Get the size of all your tables
</h2>

```sql theme={null}
SELECT table,
    formatReadableSize(sum(bytes)) as size
    FROM system.parts
    WHERE active
GROUP BY table
```

The response looks like:

```response theme={null}
┌─table───────────┬─size──────┐
│ stat            │ 38.89 MiB │
│ customers       │ 525.00 B  │
│ my_sparse_table │ 40.73 MiB │
│ crypto_prices   │ 32.18 MiB │
│ hackernews      │ 6.23 GiB  │
└─────────────────┴───────────┘
```

<h2 id="row-count-and-average-day-size-of-your-table">
  Row count and average day size of your table
</h2>

```sql theme={null}
SELECT
    table,
    formatReadableSize(size) AS size,
    rows,
    days,
    formatReadableSize(avgDaySize) AS avgDaySize
FROM
(
    SELECT
        table,
        sum(bytes) AS size,
        sum(rows) AS rows,
        min(min_date) AS min_date,
        max(max_date) AS max_date,
        max_date - min_date AS days,
        size / (max_date - min_date) AS avgDaySize
    FROM system.parts
    WHERE active
    GROUP BY table
    ORDER BY rows DESC
)
```

<h2 id="compression-columns-percentage-as-well-as-the-size-of-primary-index-in-memory">
  Compression columns percentage as well as the size of primary index in memory
</h2>

You can see how compressed your data is by column. This query also returns the size of your primary indexes in memory - useful to know because primary indexes must fit in memory.

```sql theme={null}
SELECT
    parts.*,
    columns.compressed_size,
    columns.uncompressed_size,
    columns.compression_ratio,
    columns.compression_percentage
FROM
(
    SELECT
        table,
        formatReadableSize(sum(data_uncompressed_bytes)) AS uncompressed_size,
        formatReadableSize(sum(data_compressed_bytes)) AS compressed_size,
        round(sum(data_compressed_bytes) / sum(data_uncompressed_bytes), 3) AS compression_ratio,
        round(100 - ((sum(data_compressed_bytes) * 100) / sum(data_uncompressed_bytes)), 3) AS compression_percentage
    FROM system.columns
    GROUP BY table
) AS columns
RIGHT JOIN
(
    SELECT
        table,
        sum(rows) AS rows,
        max(modification_time) AS latest_modification,
        formatReadableSize(sum(bytes)) AS disk_size,
        formatReadableSize(sum(primary_key_bytes_in_memory)) AS primary_keys_size,
        any(engine) AS engine,
        sum(bytes) AS bytes_size
    FROM system.parts
    WHERE active
    GROUP BY
        database,
        table
) AS parts ON columns.table = parts.table
ORDER BY parts.bytes_size DESC
```

<h2 id="number-of-queries-sent-by-client-in-the-last-10-minutes">
  Number of queries sent by client in the last 10 minutes
</h2>

Feel free to increase or decrease the time interval in the `toIntervalMinute(10)` function:

```sql theme={null}
SELECT
    client_name,
    count(),
    query_kind,
    toStartOfMinute(event_time) AS event_time_m
FROM system.query_log
WHERE (type = 'QueryStart') AND (event_time > (now() - toIntervalMinute(10)))
GROUP BY
    event_time_m,
    client_name,
    query_kind
ORDER BY
    event_time_m DESC,
    count() ASC
```

<h2 id="number-of-parts-in-each-partition">
  Number of parts in each partition
</h2>

```sql theme={null}
SELECT
    concat(database, '.', table),
    partition_id,
    count()
FROM system.parts
WHERE active
GROUP BY
    database,
    table,
    partition_id
```

<h2 id="finding-long-running-queries">
  Finding long running queries
</h2>

This can help find queries that are stuck:

```sql theme={null}
SELECT
    elapsed,
    initial_user,
    client_name,
    hostname(),
    query_id,
    query
FROM clusterAllReplicas(default, system.processes)
ORDER BY elapsed DESC
```

Using the query id of the worst running query, we can get a stack trace that can help when debugging.

```
SET allow_introspection_functions=1;

SELECT
    arrayStringConcat(
        arrayMap(
            x,
            y -> concat(x, ': ', y),
            arrayMap(x -> addressToLine(x), trace),
            arrayMap(x -> demangle(addressToSymbol(x)), trace)
        ),
        '\n'
    ) as trace
FROM
    system.stack_trace
WHERE
    query_id = '0bb6e88b-9b9a-4ffc-b612-5746c859e360';
```

<h2 id="view-the-most-recent-errors">
  View the most recent errors
</h2>

```
SELECT *
FROM system.errors
ORDER BY last_error_time DESC
```

The response looks like:

```response theme={null}
┌─name──────────────────┬─code─┬─value─┬─────last_error_time─┬─last_error_message──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬─last_error_trace─┬─remote─┐
│ UNKNOWN_TABLE         │   60 │     3 │ 2023-03-14 01:02:35 │ Table system.stack_trace doesn't exist                                                                                                              │ []               │      0 │
│ BAD_GET               │  170 │     1 │ 2023-03-14 00:58:55 │ Requested cluster 'default' not found                                                                                                               │ []               │      0 │
│ UNKNOWN_IDENTIFIER    │   47 │     1 │ 2023-03-14 00:49:12 │ Missing columns: 'parts.table' 'table' while processing query: 'table = parts.table', required columns: 'table' 'parts.table' 'table' 'parts.table' │ []               │      0 │
│ NO_ELEMENTS_IN_CONFIG │  139 │     2 │ 2023-03-14 00:42:11 │ Certificate file is not set.                                                                                                                        │ []               │      0 │
└───────────────────────┴──────┴───────┴─────────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴──────────────────┴────────┘
```

<h2 id="top-10-queries-that-are-using-the-most-cpu-and-memory">
  Top 10 queries that are using the most CPU and memory
</h2>

```sql theme={null}
SELECT
    type,
    event_time,
    initial_query_id,
    formatReadableSize(memory_usage) AS memory,
    `ProfileEvents.Values`[indexOf(`ProfileEvents.Names`, 'UserTimeMicroseconds')] AS userCPU,
    `ProfileEvents.Values`[indexOf(`ProfileEvents.Names`, 'SystemTimeMicroseconds')] AS systemCPU,
    normalizedQueryHash(query) AS normalized_query_hash
FROM system.query_log
ORDER BY memory_usage DESC
LIMIT 10
```

<Note>
  A single query can be logged as multiple rows with different `query_id` values — distributed secondary queries and internal view steps have `is_initial_query = 0`. Filter on `is_initial_query = 1` (or `query_id = initial_query_id`) to see queries as they were submitted, and note that the internal `query_id` values ClickHouse assigns may include a label such as `queryView...`. See the [`query_log` reference](/reference/system-tables/query_log) for details.
</Note>

<h2 id="how-much-disk-space-are-my-projection-using">
  How much disk space are my projection using
</h2>

```sql theme={null}
SELECT
    name,
    parent_name,
    formatReadableSize(bytes_on_disk) AS bytes,
    formatReadableSize(parent_bytes_on_disk) AS parent_bytes,
    bytes_on_disk / parent_bytes_on_disk AS ratio
FROM system.projection_parts
```

<h2 id="show-disk-storage-number-of-parts-number-of-rows-in-systemparts-and-marks-across-databases">
  Show disk storage, number of parts, number of rows in system.parts and marks across databases
</h2>

```sql theme={null}
SELECT
    database,
    table,
    partition,
    count() AS parts,
    formatReadableSize(sum(bytes_on_disk)) AS bytes_on_disk,
    formatReadableQuantity(sum(rows)) AS rows,
    sum(marks) AS marks
FROM system.parts
WHERE (database != 'system') AND active
GROUP BY
    database,
    table,
    partition
ORDER BY database ASC
```

<h2 id="list-details-of-recently-written-new-parts">
  List details of recently written new parts
</h2>

The details include when they got created, how large they are, how many rows, and more:

```sql theme={null}
SELECT
    modification_time,
    rows,
    formatReadableSize(bytes_on_disk),
    *
FROM clusterAllReplicas(default, system.parts)
WHERE (database = 'default') AND active AND (level = 0)
ORDER BY modification_time DESC
LIMIT 100
```

<h2 id="cluster-wide-monitoring-queries">
  Cluster-wide monitoring queries
</h2>

The following queries are useful for monitoring ClickHouse clusters. They use `clusterAllReplicas()` to aggregate data across all nodes.

<Note>
  These queries assume your cluster is named `default`. If your cluster has a different name, replace `'default'` and `default` with your cluster's actual name.
</Note>

<h3 id="avg-new-parts-per-minute-and-second">
  Average new parts created per minute and second (last hour)
</h3>

```sql theme={null}
WITH
    PER_MINUTE AS
    (
    SELECT
        toStartOfInterval(modification_time, toIntervalMinute(1)) AS t,
        count() AS new_part_count
    FROM
        clusterAllReplicas(default, merge(system, '^parts'))
    WHERE
        (database = 'default') AND
        (table = 'your_table') AND
        (active = true) AND
        (level = 0) AND
        (modification_time >= (now() - toIntervalHour(1)))
    GROUP BY
        t
    ORDER BY
        t ASC
    SETTINGS skip_unavailable_shards = 1
    )
SELECT
    AVG(new_part_count) AS new_parts_per_minute,
    new_parts_per_minute / 60 AS new_parts_per_second
FROM
    PER_MINUTE
```

Replace `'your_table'` with the actual table name you want to monitor.

<h3 id="cpu-and-memory-intensive-queries-cluster-wide">
  CPU and memory intensive queries (cluster-wide)
</h3>

```sql theme={null}
SELECT
    type,
    event_time,
    initial_query_id,
    formatReadableSize(memory_usage) AS memory,
    `ProfileEvents.Values`[indexOf(`ProfileEvents.Names`, 'UserTimeMicroseconds')] AS userCPU,
    `ProfileEvents.Values`[indexOf(`ProfileEvents.Names`, 'SystemTimeMicroseconds')] AS systemCPU,
    normalizedQueryHash(query) AS normalized_query_hash
FROM clusterAllReplicas(default, merge(system, '^query_log'))
ORDER BY memory_usage DESC
LIMIT 10
```

<h3 id="merges-in-progress-with-eta">
  Merges in progress with ETA
</h3>

This query shows currently executing merges on the cluster with estimated time to completion:

```sql theme={null}
SELECT
    hostName(),
    database,
    table,
    round(elapsed, 0) AS elapsed_seconds,
    round(progress, 4) AS progress_ratio,
    formatReadableTimeDelta((elapsed / progress) - elapsed) AS estimated_time_remaining,
    num_parts,
    result_part_name
FROM clusterAllReplicas(default, merge(system, '^merges'))
ORDER BY (elapsed / progress) - elapsed ASC
```

<h3 id="most-common-queries-by-normalized-hash">
  Most common queries by normalized hash
</h3>

Find the most frequently executed queries (useful for identifying which queries to optimize):

```sql theme={null}
SELECT
    normalizedQueryHash(query) AS query_hash,
    count() AS execution_count,
    any(query) AS example_query
FROM clusterAllReplicas(default, merge(system, '^query_log'))
WHERE event_date >= today() - 1
GROUP BY normalizedQueryHash(query)
ORDER BY execution_count DESC
LIMIT 20
```

<h3 id="error-counts-by-event-type-and-date">
  Error counts by event type and date
</h3>

Analyze part creation errors across the cluster:

```sql theme={null}
SELECT
    event_date,
    event_type,
    table,
    error,
    COUNT() AS error_count
FROM clusterAllReplicas(default, merge(system, '^part_log'))
WHERE database = 'default'
GROUP BY
    event_date,
    event_type,
    error,
    table
ORDER BY
    event_date DESC,
    error_count DESC
```

<h3 id="number-of-tables-by-node">
  Number of tables by node
</h3>

Check table distribution across cluster nodes:

```sql theme={null}
SELECT
    hostName() AS host,
    count() AS table_count
FROM clusterAllReplicas('default', merge(system, '^tables'))
WHERE database = 'default'
GROUP BY hostName()
ORDER BY table_count DESC
```

<h3 id="check-for-async-insert-operations">
  Check for async insert operations
</h3>

Monitor async insert activity:

```sql theme={null}
SELECT
    event_date,
    count() AS total_count,
    sum(if(query LIKE '%async%', 1, 0)) AS async_count,
    sum(if(query LIKE '%INSERT%', 1, 0)) AS insert_count
FROM clusterAllReplicas(default, merge(system, '^query_log'))
WHERE event_date >= today() - 7
GROUP BY event_date
ORDER BY event_date DESC
```

<h2 id="parts-and-merges-analysis">
  Parts and merges analysis
</h2>

<h3 id="currently-active-parts-by-table">
  Currently active parts by table
</h3>

See the number of active parts per table across the cluster:

```sql theme={null}
SELECT
    database,
    table,
    count() AS part_count,
    formatReadableSize(sum(bytes_on_disk)) AS total_size
FROM clusterAllReplicas(default, system.parts)
WHERE active = 1 AND database = 'default'
GROUP BY database, table
ORDER BY part_count DESC
```

<h3 id="partitions-with-too-many-parts">
  Partitions with too many parts
</h3>

Find partitions that may have too many parts (which can impact query performance):

```sql theme={null}
SELECT
    database,
    table,
    partition,
    count() AS part_count,
    formatReadableSize(sum(bytes_on_disk)) AS total_size
FROM clusterAllReplicas(default, system.parts)
WHERE active = 1
GROUP BY database, table, partition
HAVING part_count > 100
ORDER BY part_count DESC
```

<h3 id="detached-parts">
  Detached parts
</h3>

Check for detached parts that might need investigation:

```sql theme={null}
SELECT
    database,
    table,
    partition_id,
    name,
    reason,
    count()
FROM clusterAllReplicas(default, system.detached_parts)
GROUP BY database, table, partition_id, name, reason
ORDER BY database, table
```

<h2 id="system-information-queries">
  System information queries
</h2>

<h3 id="cluster-wide-memory-usage-by-node">
  Cluster-wide memory usage by node
</h3>

Monitor memory consumption across nodes:

```sql theme={null}
SELECT
    hostName() AS host,
    formatReadableSize(max(memory_usage)) AS peak_memory,
    formatReadableSize(avg(memory_usage)) AS avg_memory,
    formatReadableSize(min(memory_usage)) AS min_memory
FROM clusterAllReplicas(default, merge(system, '^query_log'))
WHERE event_date >= today() - 1
GROUP BY hostName()
ORDER BY peak_memory DESC
```

<h3 id="running-queries-on-the-cluster">
  Running queries on the cluster
</h3>

Check what queries are currently executing:

```sql theme={null}
SELECT
    hostName() AS host,
    initial_user,
    query_id,
    elapsed,
    read_rows,
    formatReadableSize(memory_usage) AS memory_usage,
    normalizedQueryHash(query) AS query_hash
FROM clusterAllReplicas(default, system.processes)
ORDER BY elapsed DESC
```

<h3 id="modified-settings-from-defaults">
  Modified settings from defaults
</h3>

See which settings have been changed from defaults:

```sql theme={null}
SELECT
    hostName() AS host,
    name,
    value
FROM clusterAllReplicas(default, system.settings)
WHERE changed = 1
ORDER BY hostName(), name
```

<h3 id="replication-queue-status">
  Replication queue status
</h3>

For replicated tables, check the replication queue:

```sql theme={null}
SELECT
    hostName() AS host,
    database,
    table,
    count() AS queue_size,
    sum(if(is_currently_executing = 1, 1, 0)) AS executing_count
FROM clusterAllReplicas(default, system.replication_queue)
GROUP BY hostName(), database, table
HAVING queue_size > 0
ORDER BY queue_size DESC
```