Common Issues to Monitor

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This page summarizes how to configure and monitor your cluster to prevent issues commonly encountered with:

• CPU
• Memory
• Storage and disk I/O

CPU

Issues with CPU most commonly arise when there is insufficient CPU to support the scale of the workload.

CPU planning

Provision enough CPU to support your operational and workload concurrency requirements:

CPU monitoring

Monitor possible signs of CPU starvation:

Service latency

Degradation in SQL response time is the most common symptom of CPU starvation. It can also be a symptom of insufficient disk I/O.

• The Service Latency: SQL Statements, 99th percentile and Service Latency: SQL Statements, 90th percentile graphs on the SQL dashboard show the time in nanoseconds between when the cluster receives a query and finishes executing the query. This time does not include returning results to the client.

If latencies are consistently high, check for:

• High CPU usage.
• An I/O bottleneck.

CPU usage

Compaction on the storage layer uses CPU to run concurrent worker threads.

• The CPU Utilization graph on the Hardware and Overload dashboards shows the CPU consumption by the CockroachDB process, and excludes other processes on the node.

  Expected values for a healthy cluster: CPU utilized by CockroachDB should not persistently exceed 80%. Because this metric does not reflect system CPU usage, values above 80% suggest that actual CPU utilization is nearing 100%.

If CPU usage is high, check whether workload concurrency is exceeding CPU resources.

Workload concurrency

The number of concurrent active SQL statements should be proportionate to your provisioned CPU.

• The SQL Queries Per Second graph on the Overview and SQL dashboards shows the 10-second moving average of SELECT, UPDATE, INSERT, and DELETE statements being executed per second on the cluster or node, as well as Total queries. The latest QPS value for the cluster is also displayed with the Queries per second counter on the Metrics page.

  Expected values for a healthy cluster: At any time, the total number of connections actively executing SQL statements should not exceed 4 times the number of vCPUs in the cluster. You can find them in the Active Executions view in the DB Console or Cloud Console. You can find the number of open connections in the DB Console or Cloud Console. For more details on configuring connection pools, see Size connection pools.

If workload concurrency exceeds CPU resources, you will observe:

• High CPU usage.
• Degradation in SQL response time.
• Over time, an unhealthy LSM and cluster instability.

LSM health

Issues at the storage layer, including an inverted LSM and high read amplification, can be observed when compaction falls behind due to insufficient CPU or excessively high recovery and rebalance rates.

• The IO Overload graph on the Overload dashboard shows the health of the persistent stores, which are implemented as log-structured merge (LSM) trees. Level 0 is the highest level of the LSM tree and consists of files containing the latest data written to the Pebble storage engine. For more information about LSM levels and how LSMs work, see Log-structured Merge-trees.

  Expected values for a healthy cluster: An IO Overload value greater than 1.0 generally indicates an overload in the Pebble LSM tree. High values indicate heavy write load that is causing accumulation of files in level 0. These files are not being compacted quickly enough to lower levels, resulting in a misshapen LSM.

  Note:

  An unhealthy LSM can be caused by other factors, including under-provisioned storage. To correlate this symptom with CPU starvation, check for high CPU usage and excessive workload concurrency.

• The Read Amplification graph on the Storage Dashboard shows the average number of disk reads per logical SQL statement, also known as the read amplification factor.

  Expected values for a healthy cluster: Read amplification factor should be in the single digits. A value exceeding 50 for 1 hour strongly suggests that the LSM tree has an unhealthy shape.

• The STORAGE logging channel indicates an unhealthy LSM with the following:

  • Frequent compaction status messages.
  • High-read-amplification warnings, e.g., sstables (read amplification = 54).

Node health

If issues at the storage layer remain unresolved, affected nodes will miss their liveness heartbeats, causing the cluster to lose nodes and eventually become unresponsive.

• The Node status on the Cluster Overview page indicates whether nodes are online (LIVE) or have crashed (SUSPECT or DEAD).

• The /health endpoint of the Cluster API returns a 500 error when a node is unhealthy.

• A Prometheus alert can notify when a node has been down for 15 minutes or more.

If nodes have shut down, this can also be caused by insufficient storage capacity.

Memory

CockroachDB is resilient to node crashes. However, frequent node restarts caused by out-of-memory (OOM) crashes can impact cluster stability and performance.

Memory planning

Provision enough memory and allocate an appropriate portion for data caching:

• Provision at least 4 GiB of RAM per vCPU.

• Disable Linux memory swapping. Over-allocating memory on production machines can lead to unexpected performance issues when pages have to be read back into memory.

• The default value for --cache is 128 MiB. For production deployments, set --cache to 25% or higher. To determine appropriate settings for --cache and --max-sql-memory, use the following formula:

  (2 * --max-sql-memory) + --cache <= 80% of system RAM
  

To help guard against OOM events, CockroachDB sets a soft memory limit using mechanisms in Go. Depending on your hardware and workload, you may not need to manually tune --max-sql-memory.

Test the configuration with a reasonable workload before deploying it to production.

For additional memory recommendations, refer to Recommended Production Settings: Memory and Recommended Production Setting: Cache and SQL memory size.

Memory monitoring

Monitor memory usage and node behavior for OOM errors:

Node process restarts

CockroachDB attempts to restart nodes after they crash. Nodes that frequently restart following an abrupt process exit may point to an underlying memory issue.

• The Node status on the Cluster Overview page indicates whether nodes are online (LIVE) or have crashed (SUSPECT or DEAD).

• When deploying on Kubernetes, the kubectl get pods output contains a RESTARTS column that tracks the number of restarts for each CockroachDB pod.

• The OPS logging channel will record a node_restart event whenever a node rejoins the cluster after being offline.

• A Prometheus alert can notify when a node has restarted more than once in the last 10 minutes.

Verify OOM errors

If you observe nodes frequently restarting, confirm that the crashes are caused by OOM errors:

• Monitor dmesg to determine if a node crashed because it ran out of memory:

  $ sudo dmesg | grep -iC 3 "cockroach"
  

  The following output indicates the node crashed due to insufficient memory:

  $ host kernel: Out of Memory: Killed process <process_id> (cockroach).
  

• When deploying on Kubernetes, run kubectl logs {pod-name} and look for OOM errors in the log messages.

If you confirm that nodes are crashing due to OOM errors, also check whether SQL queries may be responsible.

SQL memory usage

An untuned SQL query can consume significant resources and impact the performance of other workloads.

• The SQL Memory graph on the SQL dashboard shows the current amount of memory in KiB allocated to the SQL layer.

  Expected values for a healthy cluster: This value should not exceed the --max-sql-memory size. A healthy threshold is 75% of allocated --max-sql-memory.

• The "active query dump", enabled by default with the diagnostics.active_query_dumps.enabled cluster setting, is a record of anonymized active queries that is written to disk when a node is detected to be under memory pressure.

  You can use the active query dump to correlate specific queries to OOM crashes. Active query dumps have the filename activequeryprof.{date-and-time}.csv and are found in the heap_profiler directory in the configured logging directory. They are also included when running cockroach debug zip.

• A SHOW STATEMENTS statement can identify long-running queries on the cluster that may be consuming excessive memory.

• A memory budget exceeded error in the logs indicates that --max-sql-memory, the memory allocated to the SQL layer, was exceeded by the operation referenced in the error. For guidance on resolving this issue, see Common Errors.

CREATE TABLE name (first STRING(100), last STRING(100));

SET enable_experimental_alter_column_type_general = true;

ALTER TABLE name ALTER first TYPE STRING(99);

Database memory usage

CockroachDB memory usage includes both accounted memory, such as the amount allocated to --cache and --max-sql-memory; and unaccounted memory, such as uncollected Go garbage and process overhead.

• The Memory Usage graph on the Runtime dashboard shows the total memory in use by CockroachDB processes. The RSS (resident set size) metric represents actual CockroachDB memory usage from the OS/Linux/pod point of view. The Go and CGo metrics represent memory allocation and total usage from a CockroachDB point of view.

  Expected values for a healthy cluster: Go Allocated will depend on workload but should not exceed --max-sql-memory by more than 100%. CGo Allocated should not exceed the --cache size and CGo Total should not exceed the --cache size by more than 15%.

For more context on acceptable memory usage, see Suspected memory leak.

Storage and disk I/O

The cluster will underperform if storage is not provisioned or configured correctly. This can lead to further issues such as disk stalls and node shutdown.

Storage and disk planning

Provision enough storage capacity for CockroachDB data, and configure your volumes to maximize disk I/O:

Storage and disk monitoring

Monitor storage capacity and disk performance:

Storage capacity

CockroachDB requires disk space in order to accept writes and report node liveness. When a node runs out of disk space, it shuts down and cannot be restarted until space is freed up.

• The Capacity graph on the Overview and Storage dashboards shows the available and used disk capacity in the CockroachDB store.

  Expected values for a healthy cluster: Used capacity should not persistently exceed 80% of the total capacity.

• A Prometheus alert can notify when a node has less than 15% of free space remaining.

For instructions on how to free up disk space as quickly as possible after dropping a table, see How can I free up disk space that was used by a dropped table?

Disk IOPS

Insufficient disk I/O can cause poor SQL performance and potentially disk stalls.

• The Disk Ops In Progress graph on the Hardware dashboard shows the number of disk reads and writes in queue.

  Expected values for a healthy cluster: This value should be 0 or single-digit values for short periods of time. If the values persist in double digits, you may have an I/O bottleneck.

• The Linux tool iostat (part of sysstat) can be used to monitor IOPS. In the device status output, avgqu-sz corresponds to the Disk Ops In Progress metric. If service times persist in double digits on any node, this means that your storage device is saturated and is likely under-provisioned or misconfigured.

With insufficient disk I/O, you may also see:

• Degradation in SQL response time.
• An unhealthy LSM.

Node heartbeat latency

Because each node needs to update a liveness record on disk, maxing out disk bandwidth can cause liveness heartbeats to be missed.

• The Node Heartbeat Latency: 99th percentile and Node Heartbeat Latency: 90th percentile graphs on the Distributed Dashboard show the time elapsed between node liveness heartbeats.

  Expected values for a healthy cluster: Less than 100ms in addition to the network latency between nodes in the cluster.

Command commit latency

• The Command Commit Latency: 50th percentile and Command Commit Latency: 99th percentile graphs on the Storage dashboard show how quickly Raft commands are being committed by nodes in the cluster. This is a good signal of I/O load.

  Expected values for a healthy cluster: On SSDs (strongly recommended), this should be between 1 and 100 milliseconds. On HDDs, this should be no more than 1 second.

See also

• Recommended Production Settings
• Monitoring and Alerting
• Common Errors and Solutions
• Operational FAQs
• Performance Tuning Recipes
• Troubleshoot Cluster Setup
• Troubleshoot SQL Behavior
• Admission Control
• Metrics
• Alerts Page (CockroachDB Advanced)

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