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Scylla Metrics

Scylla exposes dozens of different metrics which are valuable for understanding the performance of a node, and for diagnosing performance problems when those occur. Among other things, you can see counts of requests, activity of disks, cpus and network, memory usage of different types, activity in different individual tables, and many many more metrics.

Scylla’s metrics are implemented using Seastar’s metrics infrastructure. Scylla’s code updates metrics continuously in memory variables, and then exposes them through an HTTP request, http://scyllanode:9180/metrics. The response to this request is a text file listing the metrics and their current values at the time of the query. This protocol, and the format of the response was defined by the Prometheus metric collection system and is described in detail here:

Note that the REST API in port 9180 is only devoted to publishing metrics. Scylla also has a separate and more powerful REST API on port 10000.

This very simple REST API is useful for quick scripting and development work, but in Scylla production you’d usually want to collect metrics from multiple Scylla nodes, collect a history of each metric over time, and provide a graphical UI for viewing graphs of these histories. For this purpose, we provide the separate scylla-grafana-monitoring project - see on how to install and use it. The scylla-grafana-monitoring project allows you to continuously collect metrics from several Scylla nodes into a Prometheus metric-collection server, and then to visualize these metrics using Grafana and a web browser. Prometheus and Grafana will be described in separate sections below.

Metric labels: shard, instance and type

Different Scylla nodes will have different values for each metric (e.g., scylla_cql_reads, the total number of CQL read requests). Moreover, Scylla is sharded, meaning that inside each node each core works on its own data and keeps its own separate metrics. So in the metrics output, each metric identifier contains, beyond the metric’s name, also an additional label to qualify which shard this metric comes from. For example:

scylla_cql_reads{shard="0",type="derive"} 20

In this example, this measurement comes from shard 0 (the first shard) of the node which returned this metric.

When Prometheus collects measurements from multiple nodes, it further adds an “instance” label to each measurement to remember from which node this measurement came. The “instance” label has the form ip_address:port - see for more information. Note again that the instance label is not present in the metrics exposed by Scylla (http://scyllanode:9180/metrics) but added later by Prometheus.

Saving instance and shard ids on each metric is what allows a single Prometheus server to collect metrics from many Scylla nodes and their shards. The visualization tool (such as Prometheus itself, or Grafana) can then show the metrics of different nodes and shards separately, or to calculate and display various sums - e.g., the sum on all shards of each node, or the total sum of all shards and all nodes.

The “type” label should be ignored - it appears for historic reasons (it was used by collectd) and is planned to be removed in the future.

Additional metric labels

In some cases, we have several metrics which measure the same thing but for different cases. For example, Scylla has about a dozen scheduling groups (see, and we would like to get some statistics - e.g. the scheduler queue length - separately for each of these scheduling groups.

One option is to have a dozen different metrics with different names, e.g., scylla_scheduler_queue_length_main, scylla_scheduler_queue_length_statement for the two scheduling groups called “main” and “statement”.

However, there is a second option - which we chose in this case. The second option is to have just one metric name, and qualify it by a label with a value. In this case, we have one metric name scylla_scheduler_queue_length, and metrics on different scheduling groups differ by the group label: scylla_scheduler_queue_length{group="main"} and scylla_scheduler_queue_length{group="statement"}.

Each metric reported by Scylla often has multiple labels, e.g.,

scylla_scheduler_queue_length{group="main",shard="0",type="gauge"} 0.000000

This metric has the group label, saying to which scheduling group this measurement pertains, and also shard and type labels which we described in the previous section.

Per-table metrics

Most of Scylla’s metrics are global (in each shard). Scylla also supports per-table metrics, which are maintained separately for each table in the database.

On a deployment with a large number of tables, this can result in a very large number of metrics at each time, and overwhelm Scylla’s HTTP server and/or the Prometheus server collecting these metrics. For this reason, the per-table metrics are currently disabled by default: The per-table metrics are defined in the table::set_metrics() function, and only added when the enable_keyspace_column_family_metrics flag is enabled (and it is disabled by default).

To enable this flag and the per-table metrics, you can pass the parameters --enable-keyspace-column-family-metrics 1 in the Scylla command line, or set this parameter in Scylla’s configuration file.

We are planning to rethink this approach in the future. In particular, it’s not great that we currently need to restart Scylla to make these metrics available. Scylla already maintains these per-table metrics in per-table memory variables, and we just need a way to optionally expose them through the HTTP request.

To tell the metrics of the different tables apart, each metric’s identifier contains the “ks” (keyspace) and “cf” (column family - the old name for table) as labels. For example,

scylla_column_family_pending_compaction{cf="IndexInfo",ks="system",shard="0",type="gauge"} 0.000000

Here we can see the “scylla_column_family_pending_compactions” metric measured in shard 0 of this node, for the table “IndexInfo” in keyspace “system”.

Types of metrics

Scylla metrics fall under three types: “counter”, “gauge” and “histogram”.

Most metrics are of the “counter” type. A counter metric tracks a cumulative value over objects or events that existed throughout the lifetime of the node. For example, the “total number of requests processed so far”, or “the total number of bytes written to disk”.

When visualizing counter metrics, it is often useful to look at the derivative, or rate of change, of the number, instead of at the cumulative number itself. Note that Scylla only provides the cumulative number - the visualization tool used by the user (such as Grafana mentioned earlier) is responsible for calculating the rate of change - by taking two measurements of the cumulative value at two different times, and calculating the difference of cumulative value divided by the time difference. For example, by subtracting the “total number of requests” values queried one second apart, we can show the number of requests handled during that second.

In some contexts, we call counter metrics “derive” metrics. We do this mainly for historic reasons, because our previous focus on the “collectd” metric collection daemon - which Scylla still supports but is no longer our recommended choice. Collectd has both “derive” and “counter” metrics with a subtle difference: Both indicate cumulative values, but “counter” is a sum of non-negative values, while “derive” is a sum of values which may be negative. This distinction is not important in Scylla: all our cumulative metrics are sums of non-negative values, and are monotonically increasing. So in this document we picked the term “counter” and use it exclusively.

Contrary to counter metrics which accumulate a measurement throughout the lifetime of the node, a gauge metric measures the state of objects currently existing in the system. For example, the number of requests being processed right now, the size of some queue, the amount of memory devoted now to the row cache, or the amount of disk used now for the data storage.

Gauge metrics are less common than counter metrics. When visualizing them, one usually wants to look at the metric itself rather than its rate of change. However, even for gauge metrics it is sometimes useful to visualize their derivative - for example, a user might want to visualize the rate of change to the amount of disk storage.

Internally, Scylla calculates many of the gauge metrics just like calculates counter metrics - as a cumulative value: For example, Scylla maintains a metric of the number of requests being processed right now by adding 1 to the metric when starting to process a request, and subtracting 1 when the request’s processing is complete. This metric is nevertheless labeled “gauge” because it provides a metric over currently-existing objects in the system (requests being processed), not a sum of historic information.

TODO: histogram metrics. They are described in the Prometheus document linked above.

List of metrics

Looking at the response for http://scyllanode:9180/metrics is the best way to see the list of metrics currently exposed by Scylla, because it includes a textual description in a comment above each metric.

TODO: mention source files in which a developer should add new metrics.


So far, we described Scylla’s internal metric-retrieval recapability, a REST API for retrieving the current values of all metrics from a single node. But in production, as well as more advanced debugging sessions, one usually wants to collect metrics from multiple Scylla nodes, and to collect and to graph a history of each metric over time. As already mentioned above, we provide a separate project “scylla-grafana-monitoring” which does exactly this using the Prometheus time-series database.

Prometheus is installed on a separate monitoring node (which we shall call below “monitornode”). It connects to several Scylla nodes, and saves their metrics into a time-series database. Prometheus then allows querying, analyzing, and and graphing this data, via a Web interface at:


Through this Web interface, a user can search for a metric name (type a word and see the list of all metrics with this word as part of their name), and then see the current value of this metric over all shards and nodes (the “Console” tab), and also see a graph of the value of this metric over time (the “Graph” tab).

Prometheus allows querying and graphing not only the metric itself, but also various functions and aggregates of these metrics. For example, if a user asks to graph some metric xyz the result is a graph with multiple lines, one line for each shard and node. The syntax xyz{instance="..."} will limit the lines to all shards of just one node (given the node’s IP address), and the syntax xyz{instance="...",shard="0"} will show only one shard of one node. The syntax xyz{group=~"memtable.*"} will show only metrics where the group label matches the given regular expression.

The syntax sum(xyz) will plot just one line, with the total of the metric xyz over all shards in all nodes. It’s also possible to plot partial sums - for example sum(xyz) by (group) generates a separate sum (and plot line) for each value of the label group.

The expression irate(xyz[1m]) graphs the rate of change (i.e., the derivative) of the metric xyz. In this last example, the “1m” selector is ignored by the irate() function, but some duration is required by the Prometheus syntax.

Prometheus supports many more functions and aggregations, which are described in its documentation:


While Prometheus already allows analyzing and graphing metric data, Grafana is a more advanced user interface which allows displaying many of these graphs in professional-looking “dashboards” which are more convenient for end-users who don’t know which metrics Scylla has and what they mean, and want to see pre-canned dashboards of graphs that are useful for particular purposes.

The Grafana user interface is available in: