This post outlines some of statistics and diagnostics currently gathered by InfluxDB, and some advice on how to work with this information.
Who watches the watchers?
A common use of InfluxDB is the monitoring and analysis of IT infrastructure. And to run a successful InfluxDB system, the database itself must be monitored. The command
SHOW STATS allows you to do just that.
SHOW STATS returns information about the various components of the system, for the node receiving the query. Each module exporting statistics exports a Measurement named after the module, and various series are associated with the Measurement. (The fact that it is a Measurement is important, as will be seen shortly.)
Let’s take a look at the runtime statistics, which capture details about the Go runtime.
> show stats name: runtime ------------- Alloc Frees HeapAlloc HeapIdle HeapInUse HeapObjects HeapReleased HeapSys Lookups Mallocs NumGC NumGoroutine PauseTotalNs Sys TotalAlloc 4056352 15134 4056352 1712128 4874240 7001 0 6586368 71 22135 4 51 1573952 10918136 13093576
In this case
SHOW STATS gives you an overview of memory usage by the InfluxDB system, within the Go runtime. Many Go developers will recognize the importance of these numbers.
Another key statistic is the httpd module:
name: httpd tags: bind=:8086 query_req query_resp_bytes req --------- ---------------- --- 2 418 2
This output shows the number of queries received (
query_req), by this node, since the system started – 2 in this example – and the number of bytes returned to the client, 418 in this case (this system just started!).
Most inputs, such as Graphite and openTSDB, also have detailed statistics available. This can be particularly useful when working with these systems. We get plenty of questions about performance of these inputs, so this statistical information can be really useful.
Here are example statistics for the Graphite input:
name: graphite tags: bind=:2003, proto=tcp batches_tx bytes_rx connections_active connections_handled points_rx points_tx ---------- -------- ------------------ ------------------- --------- --------- 62 1658490 6 6 69006 62000
This shows the number of points received by the Graphite service on port 2003 (
points_rx), for the TCP protocol. It also shows the number of points sent to the database (
points_tx). If you notice
points_rx is greater than
points_tx. This shows that the Graphite input is buffering points internally, as it batches writes into the database for maximum throughput.
These are just a few quick examples of what
SHOW STATS can do. Keep in mind that depending on what services are enabled, and what code paths execute within the database, you may see statistics from other components.
The internal database
All this statistical information is very useful, but is reset when the system restarts. What if we want to analyze the performance of our system over time? Of course, InfluxDB is a time series database, built especially for storing this kind of data. So the system periodically writes all statistical data to a special database called
_internal, which allows you to use the full power of InfluxQL to analyze the system itself.
Some examples may help.
If you have questions about how InfluxDB is using the Go heap, it’s easy to see how usage changes over time. For example using the
influx CLI, issue the following queries to see Go heap usage every 10 seconds.
> USE _internal Using database _internal > SHOW MEASUREMENTS name: measurements ------------------ name graphite httpd runtime shard write > SELECT HeapAlloc FROM runtime LIMIT 5 name: runtime ------------- time HeapAlloc 2015-09-18T18:40:04.199587653Z 548536 2015-09-18T18:40:14.199761008Z 3895536 2015-09-18T18:40:24.199791989Z 2057504 2015-09-18T18:40:34.19971719Z 2111680 2015-09-18T18:40:44.199490569Z 2169848
Even better, when coupled with a tool like Chronograf, you can visualize all this data.
The next example of a query, also visualized using Chronograf, shows a
derivative query of the total garbage collection (GC) pause time of the Go runtime. Since this graph shows rate-of-change, the spikes in the graph show when a GC pause took place.
Because every node in your cluster writes these statistics to the
_internal database, queries against
_internal return data for the whole cluster, which can be very useful. However, all data is tagged with the hostname and node ID, so analysis of a specific node is always possible. Shown below is
points_rx for the Graphite service on just the node with hostname
> SHOW TAG VALUES WITH key=hostname name: hostnameTagValues --------------------- hostname malthus > SELECT points_rx FROM graphite WHERE hostname='malthus' LIMIT 5 name: graphite -------------- time points_rx 2015-09-18T18:40:54.199425753Z 141001 2015-09-18T18:41:04.19947468Z 315608 2015-09-18T18:41:14.1993757Z 476001 2015-09-18T18:41:24.199438213Z 641001 2015-09-18T18:41:34.199454694Z 802001
But remember, the commands
SHOW STATS and
SHOW DIAGNOSTICS only ever return data for the node on which the query executes.
All statistics data are available in standard expvar format, if you wish to use external tools to monitor InfluxDB. This information is available at the endpoint
Diagnostic information is treated a little differently within the InfluxDB system. It’s mostly information about the system that is not necessarily numerical in format. It is important to note that diagnostic information is not stored in the
Example data is the build version of your InfluxDB and its uptime. This information is particularly useful to InfluxDB Support, so be sure to include the output of this query anytime you file a Support ticket or GitHub issue.
Example output is shown below.
> SHOW DIAGNOSTICS name: system ------------ PID currentTime started uptime 7299 2015-09-18T20:32:22.219545782Z 2015-09-18T19:54:04.069260449Z 38m18.150285438s name: build ----------- Branch Commit Version master d81618c57fae135d9b1c1a8fb3403722ceb29354 0.9.4 name: runtime ------------- GOARCH GOMAXPROCS GOOS version amd64 8 linux go1.5 name: network ------------- hostname malthus
More to come
As always, we encourage open-source developers to add statistics and diagnostics to any code they contribute, if it makes sense. We hope you find this information useful, as you work with InfluxDB. If you have any questions or concerns, come join us in our Community Slack.