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= Kudu collectl example README
:author: Kudu Team
:homepage: https://kudu.apache.org/

== Summary
This example implements a simple Java application which listens on a
TCP socket for time series data corresponding to the collectl wire protocol.
The commonly-available 'collectl' tool can be used to send example data
to the server. The data is stored in a table called `metrics` with a
pre-defined schema.

NOTE: this code is meant as an example of Java API usage and is not meant to be
a full-featured solution for storing metrics.

These instructions assume that you are running a Kudu cluster with a single Kudu
master on your local machine at the default port 7051. Otherwise, you can pass
your cluster's Kudu master addresses using '-DkuduMasters=host:port,host:port,...'.

To start the example server:

[source,bash]
----
$ mvn verify
$ java -jar target/kudu-collectl-example-1.0-SNAPSHOT.jar
----

To start collecting data, run the following command on one or more machines:

[source,bash]
----
$ collectl --export=graphite,127.0.0.1,p=/
----

(substituting '127.0.0.1' with the IP address of whichever server is running the
example program).

== Exploring the data with Impala
Assuming you have an Impala cluster running and configured to work with your
Kudu cluster, you can use Impala to run SQL queries on the data sent to Kudu
using the collectl example.

First, we need to map the table into Impala:

[source,sql]
----
CREATE EXTERNAL TABLE metrics
STORED AS KUDU
TBLPROPERTIES(
  'kudu.table_name' = 'metrics'
);
----

Then, we can run some queries:

[source,sql]
----
[mynode:21000] > select count(distinct metric) from metrics;
Query: select count(distinct metric) from metrics
+------------------------+
| count(distinct metric) |
+------------------------+
| 23                     |
+------------------------+
Fetched 1 row(s) in 0.19s
----

== Exploring the data with Spark

If you have Spark available, you can also look at the data in Kudu using Spark,

[source,bash]
----
$ spark2-shell --packages org.apache.kudu:kudu-spark2_2.11:1.14.0
----

You can then modify this example script to query the data with SparkSQL:

[source,bash]
----
import org.apache.kudu.spark.kudu._

val df = spark.read.options(Map(
  "kudu.master" -> "<kudu-master-addresses>",
  "kudu.table" -> "metrics")).kudu
df.registerTempTable("metrics")

// Print the first five values
spark.sql("select * from metrics limit 5").show()

// Calculate the average value of every host/metric pair
spark.sql("select host, metric, avg(value) from metrics group by host, metric").show()
----

`<kudu-master-addresses>` is a CSV of addresses for the masters of your
Kudu cluster.

Note that if you are still running the 'collectl' command above, you can see
the data changing in real time by re-running the queries.