Logstash In Practice

Logstash is a data collection and processing engine that ingests data through input, filter, and output pipelines. Beats are lightweight shippers that feed Logstash or Elasticsearch, boosting efficiency in containers.

Logstash pipelines process data from input to output through filters, using numerous input and filter plugins to read from files, databases, and streams, and to transform and index into Elasticsearch.

Run a simple Logstash example using a file input, grok to JSON, and a date plugin to index Apache logs with their original 2022 timestamps in Elasticsearch.

Extract and convert event dates with the date filter to ISO 8601 UTC timestamps, and set the timestamp field for accurate Elasticsearch indexing and time-based queries.

Enrich logs with geolocation data via the Logstash GeoIP plugin and GeoLite2 database, adding geo IP fields for IPv4 and IPv6 addresses with no external connectivity for Elasticsearch and Kibana.

Learn to use the mutate plugin in Logstash to rename, replace, and modify fields with ordered mutations, using values from other fields to craft final Elasticsearch documents.

Learn to handle errors in the filter phase with tag on failure to control execution flow and route corrupted documents to a separate index when grok, date, or mutate fail.

Learn to parse serialized json with the json filter in logstash, integrate grok for mixed content, and configure multi-line json handling with the file input codec and pruning.

Learn to parse key value lines with the CVE plugin in Logstash, configure the source, preprocess lines, and store results in the myobject field after transforming values to lowercase.

Explore the csv plugin in Logstash, loading csv data into Elasticsearch with the csv filter and options like auto detect column names, skip header, columns, convert, and delimiter handling.

Explore how the split plugin clones an event by dividing a field into multiple values and emitting separate events in Logstash, with default field and terminator settings for line endings.

Explore how the fingerprint plugin hashes selected fields to deduplicate documents in Elasticsearch, using a random key and methods like sha or md5, with configurable sources and formats.

Prune enables field retention in log events by using whitelist and blacklist rules with regular expressions, applied to top-level fields only, and can match field names or values.

Use the aggregate plugin to merge log lines into a single event with a time window and task id. Configure in-memory maps and timeouts to produce status code counts.

Explore commonly used filter plugins to modify, transform, and enrich data in Logstash, and dive into Elastico documentation to deepen understanding of Logstash capabilities.

Configure the file input plugin to read and monitor text files, handle rotation, and emit manageable events with line or multi-line codecs, while tuning path, exclude, and start position options.

Read how the stdin input plugin reads events from standard input, defaulting to one line per event and supporting a multi-line codec; note a 16kB buffer and configuration reload caveats.

Explore how the Logstash http poller fetches rest api data on a schedule, decoding responses into events. Configure pollers to query endpoints with methods, headers, timeouts, and authentication.

Integrate beats with Logstash and configure Metricbeat to collect system metrics, then route events to Elasticsearch or Logstash and build Kibana dashboards.

Learn to read records from a MySQL database using the Logstash JDBC plugin, configure drivers and connection strings, schedule queries, and batch results with lastvalue tracking.

Explore the most popular input plugins to consume and transform data from diverse sources, with options to run scheduled scripts or push documents to Elasticsearch via the HTTP plugin.

Explore how logstash offers output plugins with input equivalents, and learn the essential options for key output plugins, starting with the standard output plugin.

Learn the file output plugin in Logstash, which writes to a single file with no automatic rotation and supports gzip, codec, and line codec formatting for an Elasticsearch index backup.

Run multiple pipelines in a single Logstash instance using pipelines YAML and input conditions to keep events from each configuration isolated while managing configurations across files for efficient resource use.

Discover how Logstash's dead letter queue stores poison messages on disk, enabling continued processing and reprocessing via a separate pipeline using the DLQ input plugin with the Elasticsearch output plugin.

Master Logstash by modeling pipelines, testing them, and tuning results to achieve expected outcomes, while providing reviews and feedback to improve this course for future learners.