This page has instructions for handling errors and troubleshooting problems that may arise during application development.

Troubleshooting query problems

If you are not satisfied with your SQL query performance, follow the instructions in Make Queries Fast to be sure you are avoiding common performance problems like full table scans, missing indexes, etc.

If you have already optimized your SQL queries as described in Make Queries Fast and are still having issues such as:

  • Hanging or "stuck" queries
  • Queries that are slow some of the time (but not always)
  • Low throughput of queries

Take a look at Troubleshoot SQL Behavior.

Note:

If you aren't sure whether SQL query performance needs to be improved on your cluster, see Identify slow queries.

Transaction retry errors

Messages with the Postgres error code 40001 indicate that a transaction failed because it conflicted with another concurrent or recent transaction accessing the same data. The transaction needs to be retried by the client.

If your language's client driver or ORM implements transaction retry logic internally (e.g., if you are using Python and SQLAlchemy with the CockroachDB dialect), then you don't need to handle this logic from your application.

If your driver or ORM does not implement this logic, then you will need to implement a retry loop in your application.

The Python-like pseudocode below shows how to implement an application-level retry loop; it does not require your driver or ORM to implement advanced retry handling logic, so it can be used from any programming language or environment. In particular, your retry loop must:

  • Raise an error if the max_retries limit is reached
  • Retry on 40001 error codes
  • COMMIT at the end of the try block
  • Implement exponential backoff logic as shown below for best performance
while true:
    n++
    if n == max_retries:
        throw Error("did not succeed within N retries")
    try:
        # add logic here to run all your statements
        conn.exec('COMMIT')
        break
    catch error:
        if error.code != "40001":
            throw error
        else:
            # This is a retry error, so we roll back the current transaction
            # and sleep for a bit before retrying. The sleep time increases
            # for each failed transaction.  Adapted from
            # https://colintemple.com/2017/03/java-exponential-backoff/
            conn.exec('ROLLBACK');
            sleep_ms = int(((2**n) * 100) + rand( 100 - 1 ) + 1)
            sleep(sleep_ms) # Assumes your sleep() takes milliseconds
Note:

If a consistently high percentage of your transactions are resulting in transaction retry errors, then you may need to evaluate your schema design and data access patterns to find and remove sources of contention. For more information, see Understanding and Avoiding Transaction Contention.

For more information about transaction retry errors, see Transaction retries.

Unsupported SQL features

CockroachDB has support for most SQL features.

Additionally, CockroachDB supports the PostgreSQL wire protocol and the majority of its syntax. This means that existing applications can often be migrated to CockroachDB without changing application code.

However, you may encounter features of SQL or the Postgres dialect that are not supported by CockroachDB. For example, the following Postgres features are not supported:

  • Stored procedures and functions
  • Triggers
  • Events
  • User-defined functions
  • User-defined schemas
  • FULLTEXT functions and indexes
  • GEOSPATIAL functions and indexes
  • Drop primary key
  • XML Functions
  • Column-level privileges
  • XA syntax

For more information about the differences between CockroachDB and Postgres feature support, see PostgreSQL Compatibility.

For more information about the SQL standard features supported by CockroachDB, see SQL Feature Support

Troubleshooting cluster problems

As a developer, you will mostly be working with the CockroachDB SQL API.

However, you may need to access the underlying cluster to troubleshoot issues where the root cause is not your SQL, but something happening at the cluster level. Symptoms of cluster-level issues can include:

  • Cannot join a node to an existing cluster
  • Networking, client connection, or authentication issues
  • Clock sync, replication, or node liveness issues
  • Capacity planning, storage, or memory issues
  • Node decommissioning failures

For more information about how to troubleshoot cluster-level issues, see Troubleshoot Cluster Setup.

See also

Reference information related to this page:

Other common tasks:



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