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Logstash has two types of CONFIGURATION files: PIPELINE configuration files, which define the Logstash processing pipeline, and settings files, which specify OPTIONS that control Logstash startup and EXECUTION.

Logstash has two types of configuration files: pipeline configuration files, which define the Logstash processing pipeline, and settings files, which specify options that control Logstash startup and execution.

• The Logstash event processing pipeline coordinates the EXECUTION of inputs, filters, and outputs.
• Each input stage in the Logstash pipeline RUNS in its own thread. Inputs write events to a CENTRAL queue that is either in memory (default) or on disk.
• Each pipeline WORKER thread TAKES a batch of events off this queue, runs the batch of events through the configured filters, and then runs the filtered events through any outputs.

Codecs are basically streamed filters that can operate as part of an INPUT or output. Codecs enable you to EASILY separate the transport of your messages from the SERIALIZATION process. Popular codecs include json, msgpack, and plain (text).

Json: encode or decode data in the JSON format.

Multiline: merge multiple-line text EVENTS such as java exception and stack trace messages into a single event.

Codecs are basically streamed filters that can operate as part of an input or output. Codecs enable you to easily separate the transport of your messages from the serialization process. Popular codecs include json, msgpack, and plain (text).

Json: encode or decode data in the JSON format.

Multiline: merge multiple-line text events such as java exception and stack trace messages into a single event.

Outputs are the final phase of the LOGSTASH PIPELINE. An event can pass through multiple outputs, but once all output processing is complete, the event has finished its execution.

Some commonly used outputs include:

• Elastic search: send event data to Elastic search. If you’re planning to SAVE your data in an efficient, CONVENIENT, and easily query able format.
• File: write event data to a file on disk.
• Graphite: send event data to graphite, a popular open source tool for storing and graphing metrics.
• Statsd: send event data to statsd, a service that “LISTENS for statistics, like counters and timers, sent over UDP and sends aggregates to one or more pluggable backend services”. If you’re already using statsd, this could be useful for you!

Outputs are the final phase of the Logstash pipeline. An event can pass through multiple outputs, but once all output processing is complete, the event has finished its execution.

Some commonly used outputs include:

Filters are intermediary processing devices in the Logstash pipeline. You can COMBINE filters with conditionals to perform an action on an event if it meets certain criteria.

Some useful filters include:

• Grok: parse and structure arbitrary text. Grok is currently the best way in Logstash to parse unstructured log data into something structured and query able. With 120 PATTERNS built-in to Logstash, it’s more than likely you’ll find one that meets your needs!
• Mutate: perform general TRANSFORMATIONS on event fields. You can rename, remove, replace, and modify fields in your events.
• Drop: drop an event completely, for example, debug events.
• Clone: make a copy of an event, possibly adding or removing fields.
• Geoip: add INFORMATION about geographical location of IP addresses (also displays AMAZING charts in Kibana!)

Filters are intermediary processing devices in the Logstash pipeline. You can combine filters with conditionals to perform an action on an event if it meets certain criteria.

Some useful filters include:

You use inputs to get data into Logstash.

Some of the more commonly-used inputs are:

• File
• Syslog
• Redis
• Beats

File: READS from a file on the filesystem, much like the UNIX command TAIL -0F

Syslog: listens on the well-known port 514 for syslog messages and PARSES according to the RFC3164 format

Redis: reads from a redis server, using both redis channels and redis LISTS. Redis is often used as a “broker” in a CENTRALIZED Logstash installation, which queues Logstash events from remote Logstash “shippers”.

Beats: processes events sent by File beat.

You use inputs to get data into Logstash.

Some of the more commonly-used inputs are:

File: reads from a file on the filesystem, much like the UNIX command tail -0F

Syslog: listens on the well-known port 514 for syslog messages and parses according to the RFC3164 format

Redis: reads from a redis server, using both redis channels and redis lists. Redis is often used as a “broker” in a centralized Logstash installation, which queues Logstash events from remote Logstash “shippers”.

Beats: processes events sent by File beat.

The Logstash event processing pipeline has three stages:

inputs -> filters -> outputs.

Inputs generate events, filters modify them, and outputs ship them ELSEWHERE. Inputs and outputs SUPPORT codecs that enable you to encode or DECODE the data as it ENTERS or exits the pipeline without having to use a separate filter.

The Logstash event processing pipeline has three stages:

inputs -> filters -> outputs.

Inputs generate events, filters modify them, and outputs ship them elsewhere. Inputs and outputs support codecs that enable you to encode or decode the data as it enters or exits the pipeline without having to use a separate filter.

To add a Twitter feed, you use the twitter input plugin. To CONFIGURE the plugin, you need several pieces of information:

• A consumer KEY, which uniquely identifies your Twitter APP.
• A consumer secret, which serves as the PASSWORD for your Twitter app.
• One or more keywords to search in the incoming feed. The example shows using “cloud” as a keyword, but you can use whatever you want.
• An oauth token, which identifies the Twitter account using this app.
• An oauth token secret, which serves as the password of the Twitter account.

To add a Twitter feed, you use the twitter input plugin. To configure the plugin, you need several pieces of information:

Geoip plugin looks up IP addresses, DERIVES geographic location INFORMATION from the addresses, and adds that location information to the LOGS.

Geoip plugin looks up IP addresses, derives geographic location information from the addresses, and adds that location information to the logs.

The grok filter plugin enables you to PARSE the unstructured log DATA into something structured and QUERY able.

Because the grok filter plugin looks for patterns in the incoming log data, configuring the plugin requires you to make decisions about how to identify the patterns that are of interest to your USE case.

The grok filter plugin enables you to parse the unstructured log data into something structured and query able.

Because the grok filter plugin looks for patterns in the incoming log data, configuring the plugin requires you to make decisions about how to identify the patterns that are of interest to your use case.

The File beat client is a LIGHTWEIGHT, resource-friendly tool that collects logs from files on the server and forwards these logs to your Logstash instance for PROCESSING.

File beat is designed for reliability and LOW latency. File beat has a light resource footprint on the host machine, and the BEATS input plugin minimizes the resource DEMANDS on the Logstash instance.

The File beat client is a lightweight, resource-friendly tool that collects logs from files on the server and forwards these logs to your Logstash instance for processing.

File beat is designed for reliability and low latency. File beat has a light resource footprint on the host machine, and the Beats input plugin minimizes the resource demands on the Logstash instance.

A Logstash pipeline has TWO required elements, input and OUTPUT, and one OPTIONAL element, filter. The input plugins CONSUME data from a source, the filter plugins modify the data as you specify, and the output plugins write the data to a DESTINATION.

A Logstash pipeline has two required elements, input and output, and one optional element, filter. The input plugins consume data from a source, the filter plugins modify the data as you specify, and the output plugins write the data to a destination.

LOGSTASH REQUIRES JAVA 8. Java 9 is not supported.

Logstash requires Java 8. Java 9 is not supported.

Transform HTTP requests into events:

• Consume from web SERVICE firehouses like Twitter for social sentiment analysis.
• Web HOOK support for GITHUB, Hip Chat, JIRA, and countless other applications.
• Enables MANY Watcher alerting use cases.
• Create events by polling HTTP endpoints on DEMAND.
• Universally capture health, performance, metrics, and other types of data from web application interfaces.
• Perfect for scenarios where the control of polling is preferred over receiving.

Transform HTTP requests into events:

• Logs and METRICS – Logstash handle all types of logging data.
• Easily INGEST a multitude of web logs like Apache, and application logs like log4j for Java.
• Capture many other log formats like syslog, NETWORKING and firewall logs, and more.
• Enjoy complimentary secure log forwarding capabilities with FILE beat.
• Collect metrics from Ganglia, collected, NetFlow, JMX, and many other infrastructure and application platforms over TCP and UDP.

• The ingestion WORKHORSE for Elastic search and more – Horizontally scalable data processing pipeline with strong Elastic search and KIBANA synergy.
• Pluggable pipeline architecture – Mix, match, and orchestrate DIFFERENT inputs, filters, and outputs to play in pipeline harmony.
• Community-extensible and developer-friendly plugin ecosystem – Over 200 PLUGINS available, plus the flexibility of creating and contributing your own.

ELASTIC search, LOGSTASH, and Kibana, when used together is KNOWN as an ELK STACK.

Elastic search, Logstash, and Kibana, when used together is known as an ELK stack.

File BEAT is based on the Logstash Forwarder source code and replaces Logstash Forwarder as the METHOD to use for tailing LOG files and FORWARDING them to Logstash. The REGISTRY file, which stores the state of the currently read files, was changed.

File beat is based on the Logstash Forwarder source code and replaces Logstash Forwarder as the method to use for tailing log files and forwarding them to Logstash. The registry file, which stores the state of the currently read files, was changed.

Logstash is an open source tool for collecting, parsing, and storing logs for future USE. Kibana 3 is a web interface that can be USED to SEARCH and view the logs that Logstash has INDEXED. Both of these tools are BASED on Elastic search.

Logstash is an open source tool for collecting, parsing, and storing logs for future use. Kibana 3 is a web interface that can be used to search and view the logs that Logstash has indexed. Both of these tools are based on Elastic search.

Logstash is an open source data collection engine with real-time pipelining capabilities. Logstash can dynamically UNIFY data from disparate sources and NORMALIZE the data into destinations of your choice. CLEANSE and democratize all your data for DIVERSE advanced DOWNSTREAM analytics and visualization use cases.

Logstash is an open source data collection engine with real-time pipelining capabilities. Logstash can dynamically unify data from disparate sources and normalize the data into destinations of your choice. Cleanse and democratize all your data for diverse advanced downstream analytics and visualization use cases.