Why is my process hanging when I try to start it in the background?
The first question that needs to be asked is whether or not you have previously run a multi-node cluster using the same data directory. If you haven't, then you should check out our Cluster Setup Troubleshooting docs. If you have previously started and stopped a multi-node cluster and are now trying to bring it back up, you're in the right place.
In order to keep your data consistent, CockroachDB only works when at least a
majority of its nodes are running. This means that if only one node of a three
node cluster is running, that one node will not be able to do anything. The
--background flag of
cockroach start causes the start
command to wait until the node has fully initialized and is able to start
Together, these two facts mean that the
--background flag will cause
cockroach start to hang until a majority of nodes are running. In order to
restart your cluster, you should either use multiple terminals so that you can
start multiple nodes at once or start each node in the background using your
shell's functionality (e.g.,
cockroach start &) instead of the
Why is memory usage increasing despite lack of traffic?
Like most databases, CockroachDB caches the most recently accessed data in memory so that it can provide faster reads, and its periodic writes of timeseries data cause that cache size to increase until it hits its configured limit. For information about manually controlling the cache size, see Recommended Production Settings.
Why is disk usage increasing despite lack of writes?
The timeseries data used to power the graphs in the admin UI is stored within the cluster and accumulates for 30 days before it starts getting truncated. As a result, for the first 30 days or so of a cluster's life, you will see a steady increase in disk usage and the number of ranges even if you aren't writing data to the cluster yourself.
Can I reduce or disable the storage of timeseries data? New in v2.0
Reduce the interval for timeseries storage
To reduce the interval for storage of timeseries data, change the
timeseries.resolution_10s.storage_duration cluster setting to an
INTERVAL value less than
720h0m0s (30 days). For example, to store timeseries data for the last 15 days, run the following
SET CLUSTER SETTING command:
> SET CLUSTER SETTING timeseries.resolution_10s.storage_duration = '360h0m0s';
> SHOW CLUSTER SETTING timeseries.resolution_10s.storage_duration;
| timeseries.resolution_10s.storage_duration |
| 360h |
Disable timeseries storage entirely
To disable the storage of timeseries data entirely, run the following command:
> SET CLUSTER SETTING timeseries.storage.enabled = false;
> SHOW CLUSTER SETTING timeseries.storage.enabled;
| timeseries.storage.enabled |
| false |
If you want all existing timeseries data to be deleted, change the
timeseries.resolution_10s.storage_duration cluster setting as well:
> SET CLUSTER SETTING timeseries.resolution_10s.storage_duration = '0s';
Why would increasing the number of nodes not result in more operations per second?
If queries operate on different data, then increasing the number of nodes should improve the overall throughput (transactions/second or QPS).
However, if your queries operate on the same data, you may be observing transaction contention. See Understanding and Avoiding Transaction Contention for more details.
Why does CockroachDB collect anonymized cluster usage details by default?
Collecting information about CockroachDB's real world usage helps us prioritize the development of product features. We choose our default as "opt-in" to strengthen the information we receive from our collection efforts, but we also make a careful effort to send only anonymous, aggregate usage statistics. See Diagnostics Reporting for a detailed look at what information is sent and how to opt-out.
What happens when node clocks are not properly synchronized?
CockroachDB requires moderate levels of clock synchronization to preserve data consistency. For this reason, when a node detects that its clock is out of sync with at least half of the other nodes in the cluster by 80% of the maximum offset allowed (500ms by default), it spontaneously shuts down. While serializable consistency is maintained regardless of clock skew, skew outside the configured clock offset bounds can result in violations of single-key linearizability between causally dependent transactions. It's therefore important to prevent clocks from drifting too far by running NTP or other clock synchronization software on each node.
The one rare case to note is when a node's clock suddenly jumps beyond the maximum offset before the node detects it. Although extremely unlikely, this could occur, for example, when running CockroachDB inside a VM and the VM hypervisor decides to migrate the VM to different hardware with a different time. In this case, there can be a small window of time between when the node's clock becomes unsynchronized and when the node spontaneously shuts down. During this window, it would be possible for a client to read stale data and write data derived from stale reads.
For guidance on synchronizing clocks, see the tutorial for your deployment environment:
|Use NTP with Google's external NTP service.
|Use the Amazon Time Sync Service.
|Disable Hyper-V time synchronization and use NTP with Google's external NTP service.
|Use NTP with Google's external NTP service.
|Use NTP with Google's internal NTP service.
How can I tell how well node clocks are synchronized?
As explained in more detail in our monitoring documentation, each CockroachDB node exports a wide variety of metrics at
http://<host>:<http-port>/_status/vars in the format used by the popular Prometheus timeseries database. Two of these metrics export how close each node's clock is to the clock of all other nodes:
|The mean difference between the node's clock and other nodes' clocks in nanoseconds
|The standard deviation of the difference between the node's clock and other nodes' clocks in nanoseconds
As described in the above answer, a node will shut down if the mean offset of its clock from the other nodes' clocks exceeds 80% of the maximum offset allowed. It's recommended to monitor the
clock_offset_meannanos metric and alert if it's approaching the 80% threshold of your cluster's configured max offset.
You can also see these metrics in the Clock Offset graph on the Admin UI's Runtime dashboard as of the v2.0 release.
How do I prepare for planned node maintenance?
By default, if a node stays offline for more than 5 minutes, the cluster will consider it dead and will rebalance its data to other nodes. Before temporarily stopping nodes for planned maintenance (e.g., upgrading system software), if you expect any nodes to be offline for longer than 5 minutes, you can prevent the cluster from unnecessarily rebalancing data off the nodes by increasing the
server.time_until_store_dead cluster setting to match the estimated maintenance window.
For example, let's say you want to maintain a group of servers, and the nodes running on the servers may be offline for up to 15 minutes as a result. Before shutting down the nodes, you would change the
server.time_until_store_dead cluster setting as follows:
> SET CLUSTER SETTING server.time_until_store_dead = '15m0s';
After completing the maintenance work and restarting the nodes, you would then change the setting back to its default:
> SET CLUSTER SETTING server.time_until_store_dead = '5m0s';
It's also important to ensure that load balancers do not send client traffic to a node about to be shut down, even if it will only be down for a few seconds. If you find that your load balancer's health check is not always recognizing a node as unready before the node shuts down, you can increase the
server.shutdown.drain_wait setting, which tells the node to wait in an unready state for the specified duration. For example:
> SET CLUSTER SETTING server.shutdown.drain_wait = '10s';