Distributed systems produce different types of data like logs, events, and metrics. Collecting and processing these different types of data traditionally requires various tools that are difficult for developers and operations teams to maintain. Heka is a tool that can be used to intake, process, and output all these types of data using a single pipeline. It provides a standard way to handle logs, metrics and events, replacing multiple complex tools. Heka offers benefits like low resource usage, real-time analytics, and a consistent interface for developers to integrate monitoring data.

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Handling Logs, Events and
Metrics Using Heka
All rights reserved. Exotel Techcom Pvt. Ltd. © 2016
Siddharth Ramesh
sid@exotel.in / @sidramesh

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Context
Distributed systems produce different types of data
LOGS EVENTSMETRICS
Each type of data has unique (aggregation / processing / freshness) characteristics

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Why? (The Problem)
Collecting, aggregating, processing these distinct types of data
require a variety of different tools

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Why? (The Problem)
LOGS METRICS
•Logstash
•rsyslogd
•fluentd
•Splunk
•Statsd pushing into (Graphite | Influx |
OpenTSDB)
•Perf Collectors
•Datadog / Newrelic

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Events:
•There is no standard!
•Brokered Queues (SQS, RabbitMQ, Beanstalkd)
•Websockets / XMPP
Application Developer:
•Use many different libraries
DevOps Team:
•Different technologies to maintain
Why? (The Problem)

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Heka is a high performance, extremely extensible tool for
gathering, analyzing, monitoring and reporting data
•Acquire data
•Transform data
•Output data
Simple Semantics:
“Swiss Army Knife”
What? (is Heka)
Internally modeled as a generic message router

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Various Inputs
- Logs
- TCP / UDP / HTTP
- StatsD
…
Inbuilt Realtime Graphing
Complex Processing
- Aggregation
- Anomaly detection (including Alerting!)
What? (is Heka)

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•Library (currently only in Go) to emit
logs, events or metrics
•Log Input for logs, StatsD Input for
metrics, TCP Input for events
How? (do we use it)
•Only hekad runs on every machine
•Local hekad captures data and
pushes to a brokered queue (Kafka)
•Global hekad reads off Kafka
and pushes logs and metrics to
respective stores
•Soon – Push to Nagios
(Eliminate nsca)
Basically, the single data
pipeline in Exotel

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No separate libraries for sending events,
collecting perf counters
Our Experience
•Single library to integrate with
Dev win:
•Consistent interface 5 logstash Medium instances replaced
with 1 hekad medium instance
•Ability to change the underlying
pieces without dev support
DevOps win:
Always < 5% CPU utilization
•Single data pipeline for all data
•Super light-weight

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•Do ping us for any help
Our Experience
A bit tough to configure at first:
•Very light-weight (than say, Ruby plugins for Logstash)
Lua plugins FTW:
•Dynamically loaded by Heka w/o restart

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Takeaways
Tremendous value in having a single data pipeline
“One ring to rule (90% of) them all”
Heka helps you (possibly) achieve it

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Thank you
Handling Logs, Events and
Metrics Using Hekad