Version: 2.7.6

Metrics Collector API Specification

Sending Metrics to AMS (POST)

Sending metrics to Ambari Metrics Service can be achieved through the following API call.

The Sink implementations responsible for sending metrics to AMS, buffer data for 1 minute before sending. TimelineMetricCache provides a simple cache implementation to achieve this behavior.

Sample sink implementation use by Hadoop daemons: https://github.com/apache/ambari/tree/trunk/ambari-metrics/ambari-metrics-hadoop-sink

POST http://<ambari-metrics-collector>:6188/ws/v1/timeline/metrics

{
  "metrics": [
    {
      "metricname": "AMBARI_METRICS.SmokeTest.FakeMetric",
      "appid": "amssmoketestfake",
      "hostname": "ambari20-5.c.pramod-thangali.internal",
      "timestamp": 1432075898000,
      "starttime": 1432075898000,
      "metrics": {
        "1432075898000": 0.963781711428,
        "1432075899000": 1432075898000
      }
    }
  ]
}

Connecting (POST) to <ambari-metrics-collector>:6188/ws/v1/timeline/metrics/
Http response: 200 OK

Fetching Metrics from AMS (GET)

Sample call

GET http://<ambari-metrics-collector>:6188/ws/v1/timeline/metrics?metricNames=AMBARI_METRICS.SmokeTest.FakeMetric&appId=amssmoketestfake&hostname=<hostname>&precision=seconds&startTime=1432075838000&endTime=1432075959000
Http response: 200 OK
Http data:
{
   "metrics": [
      {
         "timestamp": 1432075898089,
         "metricname": "AMBARI_METRICS.SmokeTest.FakeMetric",
         "appid": "amssmoketestfake",
         "hostname": "ambari20-5.c.pramod-thangali.internal",
         "starttime": 1432075898000,
         "metrics": {
            "1432075898000": 0.963781711428,
            "1432075899000": 1432075898000
         }
      }
   ]
}

Generic GET call format

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=<>&hostname=<>&appId=<>&startTime=<>&endTime=<>&precision=<>

Query Parameters Explanation

Parameter	Optional/Mandatory	Explanation	Values it can take
metricNames	Mandatory Comma	separated list of metrics that are required.	disk_free,mem_free... etc
appId	Mandatory	The AppId that corresponds to the metricNames that were requested. Currently, only 1 AppId is required and allowed.	HOST/namenode/datanode/nimbus/hbase/kafka_broker/FLUME_HANDLER etc
hostname	Optional	Comma separated list of hostnames. When no specified, cluster aggregates are returned.	h1,h2..etc
startTime, endTime	Optional	Start and End time values. If not specified, the last data point of the metric is returned.	epoch times in seconds or milliseconds
precision	Optional	What precision the data needs to be returned. If not specified, the precision is calculated based on the time range requested (Table below)	SECONDS/MINUTES/DAYS/HOURS

Precision query parameter (Default resolution)

Query Time range	Resolution of returned metrics	Comments
Upto 2 hours	SECONDS	10 second data for host metrics 30 second data for Aggregated query (No host specified)
2 hours - 1 day	MINUTES	5 minute data
1 day - 30 days	HOURS	1 hour data
> 30 days	DAYS	1 day data

Specifying Aggregate Functions

The metricName can have a specific aggregate function qualifier after the metricName (as shown below) to request specific aggregates. Valid values are ._avg, ._max, ._min, ._sum. When an aggregate query is requested without an aggregate function in the metricName, the default is AVG. Examples

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.totalRequestCount._avg,regionserver.Server.writeRequestCount._max&appId=hbase&startTime=14000000&endTime=14200000

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.readRequestCount,regionserver.Server.writeRequestCount._max&appId=hbase&startTime=14000000&endTime=14200000

Specifying Post processing Functions

Similar to aggregate functions, post processing functions can also be specified. Currently, we have 2 post processing functions - rate (Rate per second) and diff (difference between consecutive values). Post processing functions can also be applied after aggregate functions. Examples

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.totalRequestCount._rate,regionserver.Server.writeRequestCount._diff&appId=hbase&startTime=14000000&endTime=14200000

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.readRequestCount._max._diff&appId=hbase&startTime=14000000&endTime=14200000

Specifying Wild Cards

Both metricNames and hostname take wildcard (%) values for a group of metric (or hosts). A query can have a combination of full metric names and names with wildcards also.

Examples

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.%&appId=hbase&startTime=14000000&endTime=14200000

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.%&hostname=abc.testdomain124.devlocal&appId=hbase&startTime=14000000&endTime=14200000

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=master.AssignmentManger.ritCount,regionserver.Server.%&hostname=abc.testdomain124.devlocal&appId=hbase&startTime=14000000&endTime=14200000

http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.%&hostname=abc.testdomain12%.devlocal&appId=hbase&startTime=14000000&endTime=14200000

Downsampling

As discussed before, AMS downsamples data when higher time ranges are requested. The default "downsampled across time" data returned is AVG. Specific downsamples can be requested by adding the aggregate function qualifiers ( ._avg, ._max, ._min, ._sum ) to the metric names the same way like requesting aggregates across the cluster. Example

 http://<AMS_HOST>:6188/ws/v1/timeline/metrics?metricNames=regionserver.Server.totalRequestCount._max&hostname=abc.testdomain124.devlocal&appId=hbase&startTime=14000000&endTime=14200000&precision=MINUTES

The above query returns 5 minute data for the metric, where the data point value is the MAX of the values found in every 5 minute range.

AMS Metadata API

AMS has 2 metadata endpoints that are useful for finding out the set of metrics it received, as well as the topology of the cluster.

METRICS METADATA

Endpoint :

 http://<AMS_HOST>:6188/ws/v1/timeline/metrics/metadata

Data returned : A mapping between the set of APP_IDs to the list of metrics received with that AppId.

Sample data returned

HOSTS METADATA

Endpoint :

 http://<AMS_HOST>:6188/ws/v1/timeline/metrics/hosts

Data returned : A mapping between the hosts in the cluster and the set of APP_IDs on the host.

Sample data returned

Guide to writing your own Sink

Include the ambari-metrics-common artifacts from source or maven-central (when available) into your project
Find below helpful info regarding common data-structures to use from the ambari-metrics-common module
Extend the org.apache.hadoop.metrics2.sink.timeline.AbstractTimelineMetricsSink class and implement the required methods
Use the org.apache.hadoop.metrics2.sink.timeline.cache.TimelineMetricsCache to store intermediate data until it is time to send (example: collection interval = 10 seconds, send interval = 1 minute). The cache implementation provides the logic needed for buffering and local aggregation.
Use org.apache.hadoop.metrics2.sink.timeline.AbstractTimelineMetricsSink#emitMetrics to send metrics to AMS backend.

METRIC DATA STRUCTURE

Source location for common data structures module: https://github.com/apache/ambari/tree/trunk/ambari-metrics/ambari-metrics-common/

Example sink implementation: https://github.com/apache/ambari/blob/trunk/ambari-metrics/ambari-metrics-hadoop-sink/

INTERNAL PHOENIX KEY STRUCTURE

The Metric Record Key data structure is described below:

Property	Type	Comment	Optional
Metric Name	String	First key part, important consideration while querying from HFile storage	N
Hostname	String	Second key part	N
Server time	Long	Timestamp on server when first metric write request was received	N
Application Id	String	Uniquely identify service	N
Instance Id	String	Second key part to identify instance/ component	Y
Start time	Long	Start of the timeseries data

HOW AGGREGATION WORKS

The granularity of aggregate data can be controlled by setting wake up interval for each of the aggregator threads.
Presently we support 2 types of aggregators, HOST and APPLICATION with 3 time dimensions, per minute, per hour and per day.
- The HOST aggregates are just aggregates on precision data across the supported time dimensions.
- The APP aggregates are across appId. Note: We ignore instanceId for APP level aggregates. Same time dimensions apply for APP level aggregates.
- We also support HOST level metrics for APP, meaning you can expect a system metric example: "cpu_user" to be aggregated across datanodes, effectively calculating system metric for hosted apps.
Each aggregator performs checkpointing by storing last successful time of completion in a file. If the checkpoint is too old, the aggregators will discard checkpoint and aggregate data for the configured interval, meaning data in between (now - interval) time.
Refer to Phoenix table schema for details of tables and records.

Metrics Collector API Specification

Sending Metrics to AMS (POST)​

Fetching Metrics from AMS (GET)​

AMS Metadata API​

Guide to writing your own Sink​

Sending Metrics to AMS (POST)

Fetching Metrics from AMS (GET)

AMS Metadata API

Guide to writing your own Sink