How to: HipChat Alerting for Elasticsearch with ElastAlert

Posted by Adam Vanderbush May 27, 2017

In the previous tutorial in ElastAlert Series, we implemented new_term, change and spike rules for ElastAlert alerting via Slack. We will next be looking into configuring and setting up alerting using ElastAlert on to the popular cloud-based team collaboration tool HipChat.

Many organisations use Elasticsearch to rapidly prototype and launch new search applications, and moving quickly at scale raises challenges. In particular, we often encounter difficulty making changes to query logic without impacting users, as well as finding client library bugs, problems with multi-tenancy, and general reliability issues. As the number of queries grow, the Search Infrastructure faces difficulty in supporting the multitude of ways queries are being sent to Elasticsearch cluster. The infrastructure designed for a single team to communicate with a single cluster does not scale to tens of teams and tens of clusters.

Indexing in large volumes require instantaneous alerting on anomalies, spikes, or other patterns of interest from data in Elasticsearch. If you have data being written into Elasticsearch in near real time and want to be alerted when that data matches certain patterns, ElastAlert is the tool for you.

For this post, we will be using hosted Elasticsearch on Qbox.io. You can sign up or launch your cluster here, or click “Get Started” in the header navigation. If you need help setting up, refer to “Provisioning a Qbox Elasticsearch Cluster.“ 

ElastAlert is now available on Qbox provisioned Elasticsearch clusters and can be easily configured. Implementing ElastAlert is easy on Qbox. When you provision a cluster, there is a configuration box where you can input your Alert rules.  If you’re unclear how to structure rules in YAML, be sure to consult the ElastAlert Documentation.

Our Goal

The goal of the tutorial is to use Qbox as a Centralized Logging, Alerting and Monitoring solution to automatically raise alerts on HipChat Channel. We will assume you do have a HipChat account set-up and running. Qbox provides out of box solution for Elasticsearch, Kibana and many of Elasticsearch analysis and monitoring plugins. We will set up Logstash in a separate node or machine to gather twitter stream and use Qbox provisioned ElastAlert alerting to configure rules and set up alerts for detection of anomalies and inconsistencies in data.

Our ELK stack setup has four main components:

  • Elasticsearch: It is used to store all of the application and monitoring logs(Provisioned by Qbox).
  • Logstash: The server component that processes incoming logs and feeds to ES.
  • ElastAlert:  The superb open-source alerting tool built by the team at Yelp Engineering and now available on all new Elasticsearch clusters on AWS.
  • Kibana(optional): A web interface for searching and visualising logs (Provisioned by Qbox).

Prerequisites

The amount of CPU, RAM, and storage that your Elasticsearch Server will require depends on the volume of logs that you intend to gather. For this tutorial, we will be using a Qbox provisioned Elasticsearch with the following minimum specs:

  • Provider: AWS
  • Version: 5.1.1
  • RAM: 1GB
  • CPU: vCPU1
  • Replicas: 0

The above specs can be changed per your desired requirements. Please select the appropriate names, versions, regions for your needs. For this example, we used Elasticsearch version 5.1.1, the most current version is 5.3. We support all versions of Elasticsearch on Qbox. (To learn more about the major differences between 2.x and 5.x, click here.)  

In addition to our Elasticsearch Server, we will require a separate logstash server to process incoming twitter stream from twitter API and ship them to Elasticsearch. For simplicity and testing purposes, the logstash server can also act as the client server itself. The Endpoint and Transport addresses for our Qbox provisioned Elasticsearch cluster are as follows:

common_1.png

Endpoint: REST API

https://ec18487808b6908009d3:efcec6a1e0@eb843037.qb0x.com:32563

Authentication

  • Username = ec18487808b6908009d3
  • Password = efcec6a1e0

TRANSPORT (NATIVE JAVA)

eb843037.qb0x.com:30543

Note: Please make sure to whitelist the logstash server IP from Qbox Elasticsearch cluster.

Configure Alerting

Now, let’s create the “rules” namely

  • Twitter cardinality rule of type cardinality
  • Twitter percentage match rule of type percentage_match
  • Twitter single metric aggregation rule of type metric_aggregation

Using these, we will test 3 types of rules that Elastalert can manage:

  • The cardinality rule matches when a the total number of unique values for a certain field within a time frame is higher or lower than a threshold.
  • The percentage match rule matches when the percentage of document in the match bucket within a calculation window is higher or lower than a threshold. By default the calculation window is buffer_time.
  • The metric aggregation rule matches when the value of a metric within the calculation window is higher or lower than a threshold. By default, this is buffer_time.

First, we create the cardinality rule, by configuring the following code:

# Alert when a the total number of unique values for a certain field (language) for non-truncated tweets within a time frame (1 hour) is lower than a threshold (5)
# (Required)
# Rule name, must be unique
name: Event Cardinality rule
# (Required)
# Type of alert.
type: cardinality
# (Required, cardinality specific)
# Count the number of unique values for this field
cardinality_field: "language"
# (Required, frequency specific)
# If the cardinality of the data is lower than this number, an alert will be triggered. The timeframe must have elapsed since the first event before any alerts will be sent.
# Alert when there are less than 5 unique languages
min_cardinality: 5
# If the cardinality of the data is greater than this number, an alert will be triggered.
# Alert when there are more than 10 unique languages
# max_cardinality: 10
# (Required, frequency specific)
# The cardinality is defined as the number of unique values for the most recent 1 hour
timeframe:
 hours: 1
# (Required)
# A list of Elasticsearch filters used for find events
# These filters are joined with AND and nested in a filtered query
filter:
- term:
   truncated: "false"
# (Required)
# The alert is used when a match is found
alert:
- "hipchat"
hipchat:
# The randomly generated notification token created by HipChat.
hipchat_auth_token: "YfkUkoUnQ8R3mxXXXXPKOf2szt4myllDpokM"
# The id associated with the HipChat room you want to send the alert to
hipchat_room_id: "33XXX11"

HipChat Accounts API h1.png

HipChat Accounts Token Generation

h2.png

HipChat Room – Qbox

h3.png

HipChat Room – Qbox Details

h4.png

As we can see, this is a very straightforward and simple configuration. For the <strong>percentage</strong> match config, we configure our rule as follows:

# Alert when 95% of tweets in any particular language are truncated within buffer_time
# (Required)
# Rule name, must be unique
name: Event Percentage Match rule
# (Required)
# Type of alert
type: percentage_match
index: twitter-*
description: "95% of tweets in any particular language should be truncated"
# (Required)
# A list of Elasticsearch filters used for find events
# These filters are joined with AND and nested in a filtered query
filter:
- term:
  _type: twitter_logs
# default the calculation window
buffer_time:
 minutes: 5
# Group percentage by this field. For each unique value of the query_key field, the percentage will be calculated and evaluated separately against the threshold(s).
query_key: language
doc_type: twitter_logs
match_bucket_filter:
- term:
   truncated: true
# If the percentage of matching documents is greater than this number, an alert will be triggered
min_percentage: 95
#max_percentage: 60
# (Required)
# The alert is used when a match is found
alert:
- "hipchat"
hipchat:
# The randomly generated notification token created by HipChat.
hipchat_auth_token: "YfkUkoUnQ8R3mxXXXXPKOf2szt4myllDpokM"
# The id associated with the HipChat room you want to send the alert to
hipchat_room_id: "33XXX11"

Finally, let’s configure our final metric_aggregation rule as follows:

# Alert when average retweet count for a particular user’s tweet is either less than 3 or greater than 5
# (Required)
# Rule name, must be unique
name: Event Twitter Metric Aggregation Rule
# (Required)
# Type of alert
type: metric_aggregation
index: twitter-*
# default the calculation window
buffer_time:
 hours: 1
# name of the field over which the metric value will be calculated
metric_agg_key: retweet_count
# The type of metric aggregation to perform on the metric_agg_key field
metric_agg_type: avg
# Group metric calculations by this field. For each unique value of the query_key field, the metric will be calculated and evaluated separately against the threshold(s).
query_key: user.id
doc_type: twitter_logs
 
bucket_interval:
 minutes: 5
 
sync_bucket_interval: true
# If the calculated metric value is greater than this number, an alert will be triggered. This threshold is exclusive.
max_threshold: 5
# If the calculated metric value is smaller than this number, an alert will be triggered. This threshold is exclusive.
min_threshold: 3
# (Required)
# A list of Elasticsearch filters used for find events
# These filters are joined with AND and nested in a filtered query
filter:
- term:
   truncated: true
# (Required)
# The alert is used when a match is found
alert:
- "hipchat"
hipchat:
# The randomly generated notification token created by HipChat.
hipchat_auth_token: "YfkUkoUnQ8R3mxXXXXPKOf2szt4myllDpokM"
# The id associated with the HipChat room you want to send the alert to
hipchat_room_id: "33XXX11"

Thus, Qbox Configuration for Alerting must be as follows:

name: Event Cardinality rule
type: cardinality
cardinality_field: "language"
min_cardinality: 5
timeframe:
 hours: 2
filter:
- term:
   truncated: "false"
alert:
- "hipchat"
hipchat:
hipchat_auth_token: "YfkUkoUnQ8R3mxXXXXPKOf2szt4myllDpokM"
hipchat_room_id: "33XXX11"
---
name: Event Percentage Match rule
type: percentage_match
index: twitter-*
description: "95% of all truncated tweets should be in any particular language"
filter:
- term:
  _type: twitter_logs
buffer_time:
 minutes: 5
query_key: language
doc_type: twitter_logs
match_bucket_filter:
- term:
   truncated: true
min_percentage: 95
alert:
- "hipchat"
hipchat:
hipchat_auth_token: "YfkUkoUnQ8R3mxXXXXPKOf2szt4myllDpokM"
hipchat_room_id: "33XXX11"
---
name: Event Twitter Metric Aggregation Rule
type: metric_aggregation
index: twitter-*
buffer_time:
 hours: 1
metric_agg_key: retweet_count
metric_agg_type: avg
query_key: user.id
doc_type: twitter_logs  
bucket_interval:
 minutes: 5
sync_bucket_interval: true
max_threshold: 5
min_threshold: 3
filter:
- term:
   truncated: true
alert:
- "hipchat"
hipchat:
hipchat_auth_token: "YfkUkoUnQ8R3mxXXXXPKOf2szt4myllDpokM"
hipchat_room_id: "33XXX11"

Install Logstash

Download and install the Public Signing Key:

wget -qO - https://packages.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -

We will use the Logstash version 2.4.x as compatible with our Elasticsearch version 5.1.x. The Elastic Community Product Support Matrix can be referred in order to clear any version issues.

Add the repository definition to your /etc/apt/sources.list file:

echo "deb https://packages.elastic.co/logstash/2.4/debian stable main" | sudo tee -a /etc/apt/sources.list

Run sudo apt-get update and the repository is ready for use. You can install it with:

sudo apt-get update && sudo apt-get install logstash

Alternatively, logstash tar can also be downloaded from Elastic Product Releases Site. Then, the steps of setting up and running logstash are pretty simple:

  • Download and unzip Logstash
  • Prepare a logstash.conf config file
  • Run bin/logstash -f logstash.conf -t to check config (logstash.conf)
  • Run bin/logstash -f logstash.conf

Configure Logstash (Twitter Stream)

Logstash configuration files are in the JSON-format, and reside in /etc/logstash/conf.d. The configuration consists of three sections: inputs, filters, and outputs.

We need to be authorized to take data from Twitter via its API. This part is easy:

  1. Login to your Twitter account
  2. Go to https://dev.twitter.com/apps/
  3. Create a new Twitter application (here I give Twitter-Qbox-Stream as the name of the app).

t1.png

After you successfully create the Twitter application, you get the following parameters in “Keys and Access Tokens”:

  1. Consumer Key (API Key)
  2. Consumer Secret (API Secret)
  3. Access Token
  4. Access Token Secret

t2.png

We are now ready to create the Twitter data path (stream) from Twitter servers to our machine. We will use the above four parameters (consumer key, consumer secret, access token, access token secret) to configure twitter input for logstash.

Let’s create a configuration file called 02-twitter-input.conf and set up our “twitter” input:

sudo vi /etc/logstash/conf.d/02-twitter-input.conf

Insert the following input configuration:

input {
 twitter {
   consumer_key => "BCgpJwYPDjXXXXXX80JpU0"
   consumer_secret => "Eufyx0RxslO81jpRuXXXXXXXMlL8ysLpuHQRTb0Fvh2"
   keywords => ["mobile", "java", "android", "elasticsearch", "search"]
   oauth_token => "193562229-o0CgXXXXXXXX0e9OQOob3Ubo0lDj2v7g1ZR"
   oauth_token_secret => "xkb6I4JJmnvaKv4WXXXXXXXXS342TGO6y0bQE7U"
 }
}

Save and quit the file 02-twitter-input.conf.

This specifies a twitter input that will filter tweets with keywords “mobile“, “java“, “android“, “elasticsearch“, “search” and pass them to logstash output. Save and quit. Lastly, we will create a configuration file called 30-elasticsearch-output.conf:

sudo vi /etc/logstash/conf.d/30-elasticsearch-output.conf

Insert the following output configuration:

output {
 elasticsearch {
   hosts => ["https://eb843037.qb0x.com:32563/"]
   user => "ec18487808b6908009d3"
   password => "efcec6a1e0"
   index => "twitter-%{+YYYY.MM.dd}"
   document_type => "twitter_logs"
 }
 stdout { codec => rubydebug }
}

Save and exit. This output basically configures Logstash to store the twitter logs data in Elasticsearch which is running at https://eb843037.qb0x.com:30024/, in an index named after the twitter.

If you have downloaded logstash tar or zip, you can create a logstash.conf file having input, filter and output all in one place.

sudo vi LOGSTASH_HOME/logstash.conf

Insert the following input and output configuration in logstash.conf

input {
 twitter {
   consumer_key => "BCgpJwYPDjXXXXXX80JpU0"
   consumer_secret => "Eufyx0RxslO81jpRuXXXXXXXMlL8ysLpuHQRTb0Fvh2"
   keywords => ["mobile", "java", "android", "elasticsearch", "search"]
   oauth_token => "193562229-o0CgXXXXXXXX0e9OQOob3Ubo0lDj2v7g1ZR"
   oauth_token_secret => "xkb6I4JJmnvaKv4WXXXXXXXXS342TGO6y0bQE7U"
 }
}
output {
 elasticsearch {
   hosts => ["https://eb843037.qb0x.com:32563/"]
   user => "ec18487808b6908009d3"
   password => "efcec6a1e0"
   index => "twitter-%{+YYYY.MM.dd}"
   document_type => "twitter_logs"
 }
 stdout { codec => rubydebug }
}

Test your Logstash configuration with this command:

sudo service logstash configtest

It should display Configuration OK if there are no syntax errors. Otherwise, try and read the error output to see what’s wrong with your Logstash configuration.

Restart Logstash, and enable it, to put our configuration changes into effect:

sudo service logstash restart
sudo update-rc.d logstash defaults 96 9

If you have downloaded logstash tar or zip, it can be run using following command

bin/logstash -f logstash.conf

Numerous responses are received. The structure of document is as follows:

{
 "text": "Learn how to automate anomaly detection on your #Elasticsearch #timeseries data with #MachineLearning:",
 "created_at": "2017-05-07T07:54:47.000Z",
 "source": "<a href="%5C">Twitter for iPhone</a>",
 "truncated": false,
 "language": "en",
 "mention": [],
 "retweet_count": 0,
 "hashtag": [
   {
     "text": "Elasticsearch",
     "start": 49,
     "end": 62
   },
   {
     "text": "timeseries",
     "start": 65,
     "end": 75
   },
   {
     "text": "MachineLearning",
     "start": 88,
     "end": 102
   }
 ],
 "location": {
   "lat": 33.686657,
   "lon": -117.674558
 },
 "place": {
   "id": "74a60733a8b5f7f9",
   "name": "elastic",
   "type": "city",
   "full_name": "San Francisco, CA",
   "street_address": null,
   "country": "United States",
   "country_code": "US",
   "url": "https://api.twitter.com/1.1/geo/id/74a60733a8b5f7f9.json"
 },
 "link": [],
 "user": {
   "id": 2873953509,
   "name": "Elastic",
   "screen_name": "elastic",
   "location": "SF, CA",
   "description": "The company behind the Elastic Stack (#elasticsearch, #kibana, Beats, #logstash), X-Pack, and Elastic Cloud"
 }
}

The simplest of the rules to test it out is the metric_aggregation rule. All we have to do is wait for about 5 minutes with Elasticsearch running and Logstash stopped, so no documents are streaming. After the wait, we can see on our channel that an alert is received on the channel.

And, as the time passes, we will receive other alerts as well, like the percentage_match alert.

Conclusion

ElastAlert helps to learn a lot from data and use it to monitor many critical systems. If you know what you’re looking for, archiving log files and retrieving them manually might be sufficient, but this process is tedious. As your infrastructure scales, so does the volume of log files, and the need for a log management system becomes apparent. Qbox provisioned Elasticsearch is already very successful for indexing logs, faster retrieval, powerful search tools, great visualizations and many other purposes. Qbox built in support for ElastAlert will help greatly in alerting on anomalies, spikes, or other patterns of interest from data in Elasticsearch.

There are several types of included alerters. Of course, as in the example, you can send emails. You can also open JIRA issues, run arbitrary commands, and custom python code. Each alerter has it’s own specific options, but there are several that can apply to any type, such as realert, which is the minimum time before sending a subsequent alert for a given rule, and aggregation, which allows you to aggregate all alerts which occur within a timeframe for a rule together.

Other Helpful Tutorials

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