Apache Druid
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Getting started

  • Introduction to Apache Druid
  • Quickstart (local)
  • Single server deployment
  • Clustered deployment

Tutorials

  • Load files natively
  • Load files using SQL ๐Ÿ†•
  • Load from Apache Kafka
  • Load from Apache Hadoop
  • Querying data
  • Roll-up
  • Theta sketches
  • Configuring data retention
  • Updating existing data
  • Compacting segments
  • Deleting data
  • Writing an ingestion spec
  • Transforming input data
  • Tutorial: Run with Docker
  • Kerberized HDFS deep storage
  • Convert ingestion spec to SQL
  • Jupyter Notebook tutorials

Design

  • Design
  • Segments
  • Processes and servers
  • Deep storage
  • Metadata storage
  • ZooKeeper

Ingestion

  • Ingestion
  • Data formats
  • Data model
  • Data rollup
  • Partitioning
  • Ingestion spec
  • Schema design tips
  • Stream ingestion

    • Apache Kafka ingestion
    • Apache Kafka supervisor
    • Apache Kafka operations
    • Amazon Kinesis

    Batch ingestion

    • Native batch
    • Native batch: input sources
    • Migrate from firehose
    • Hadoop-based

    SQL-based ingestion ๐Ÿ†•

    • Overview
    • Key concepts
    • API
    • Security
    • Examples
    • Reference
    • Known issues
  • Task reference
  • Troubleshooting FAQ

Data management

  • Overview
  • Data updates
  • Data deletion
  • Schema changes
  • Compaction
  • Automatic compaction

Querying

    Druid SQL

    • Overview and syntax
    • SQL data types
    • Operators
    • Scalar functions
    • Aggregation functions
    • Multi-value string functions
    • JSON functions
    • All functions
    • Druid SQL API
    • JDBC driver API
    • SQL query context
    • SQL metadata tables
    • SQL query translation
  • Native queries
  • Query execution
  • Troubleshooting
  • Concepts

    • Datasources
    • Joins
    • Lookups
    • Multi-value dimensions
    • Nested columns
    • Multitenancy
    • Query caching
    • Using query caching
    • Query context

    Native query types

    • Timeseries
    • TopN
    • GroupBy
    • Scan
    • Search
    • TimeBoundary
    • SegmentMetadata
    • DatasourceMetadata

    Native query components

    • Filters
    • Granularities
    • Dimensions
    • Aggregations
    • Post-aggregations
    • Expressions
    • Having filters (groupBy)
    • Sorting and limiting (groupBy)
    • Sorting (topN)
    • String comparators
    • Virtual columns
    • Spatial filters

Configuration

  • Configuration reference
  • Extensions
  • Logging

Operations

  • Web console
  • Java runtime
  • Security

    • Security overview
    • User authentication and authorization
    • LDAP auth
    • Password providers
    • Dynamic Config Providers
    • TLS support

    Performance tuning

    • Basic cluster tuning
    • Segment size optimization
    • Mixed workloads
    • HTTP compression
    • Automated metadata cleanup

    Monitoring

    • Request logging
    • Metrics
    • Alerts
  • API reference
  • High availability
  • Rolling updates
  • Using rules to drop and retain data
  • Working with different versions of Apache Hadoop
  • Misc

    • dump-segment tool
    • reset-cluster tool
    • insert-segment-to-db tool
    • pull-deps tool
    • Deep storage migration
    • Export Metadata Tool
    • Metadata Migration
    • Content for build.sbt

Development

  • Developing on Druid
  • Creating extensions
  • JavaScript functionality
  • Build from source
  • Versioning
  • Experimental features

Misc

  • Papers

Hidden

  • Apache Druid vs Elasticsearch
  • Apache Druid vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)
  • Apache Druid vs Kudu
  • Apache Druid vs Redshift
  • Apache Druid vs Spark
  • Apache Druid vs SQL-on-Hadoop
  • Authentication and Authorization
  • Broker
  • Coordinator Process
  • Historical Process
  • Indexer Process
  • Indexing Service
  • MiddleManager Process
  • Overlord Process
  • Router Process
  • Peons
  • Approximate Histogram aggregators
  • Apache Avro
  • Microsoft Azure
  • Bloom Filter
  • DataSketches extension
  • DataSketches HLL Sketch module
  • DataSketches Quantiles Sketch module
  • DataSketches Theta Sketch module
  • DataSketches Tuple Sketch module
  • Basic Security
  • Kerberos
  • Cached Lookup Module
  • Apache Ranger Security
  • Google Cloud Storage
  • HDFS
  • Apache Kafka Lookups
  • Globally Cached Lookups
  • MySQL Metadata Store
  • ORC Extension
  • Druid pac4j based Security extension
  • Apache Parquet Extension
  • PostgreSQL Metadata Store
  • Protobuf
  • S3-compatible
  • Simple SSLContext Provider Module
  • Stats aggregator
  • Test Stats Aggregators
  • Druid AWS RDS Module
  • Kubernetes
  • Ambari Metrics Emitter
  • Apache Cassandra
  • Rackspace Cloud Files
  • DistinctCount Aggregator
  • Graphite Emitter
  • InfluxDB Line Protocol Parser
  • InfluxDB Emitter
  • Kafka Emitter
  • Materialized View
  • Moment Sketches for Approximate Quantiles module
  • Moving Average Query
  • OpenTSDB Emitter
  • Druid Redis Cache
  • Microsoft SQLServer
  • StatsD Emitter
  • T-Digest Quantiles Sketch module
  • Thrift
  • Timestamp Min/Max aggregators
  • GCE Extensions
  • Aliyun OSS
  • Prometheus Emitter
  • kubernetes
  • Cardinality/HyperUnique aggregators
  • Select
  • Firehose (deprecated)
  • Native batch (simple)
  • Realtime Process
Edit

Prometheus Emitter

To use this Apache Druid extension, include prometheus-emitter in the extensions load list.

Introduction

This extension exposes Druid metrics for collection by a Prometheus server (https://prometheus.io/).

Emitter is enabled by setting druid.emitter=prometheus configs or include prometheus in the composing emitter list.

Configuration

All the configuration parameters for the Prometheus emitter are under druid.emitter.prometheus.

propertydescriptionrequired?default
druid.emitter.prometheus.strategyThe strategy to expose prometheus metrics.
Should be one of exporter and pushgateway. Default strategy exporter would expose metrics for scraping purpose. Peon tasks (short-lived jobs) should use pushgateway strategy.
yesexporter
druid.emitter.prometheus.portThe port on which to expose the prometheus HTTPServer. Required if using exporter strategy.nonone
druid.emitter.prometheus.namespaceOptional metric namespace. Must match the regex [a-zA-Z_:][a-zA-Z0-9_:]*nodruid
druid.emitter.prometheus.dimensionMapPathJSON file defining the Prometheus metric type, desired dimensions, help text, and conversionFactor for every Druid metric.noDefault mapping provided. See below.
druid.emitter.prometheus.addHostAsLabelFlag to include the hostname as a prometheus label.nofalse
druid.emitter.prometheus.addServiceAsLabelFlag to include the druid service name (e.g. druid/broker, druid/coordinator, etc.) as a prometheus label.nofalse
druid.emitter.prometheus.pushGatewayAddressPushgateway address. Required if using pushgateway strategy.nonone
druid.emitter.prometheus.flushPeriodEmit metrics to Pushgateway every flushPeriod seconds. Required if pushgateway strategy is used.no15

Ports for colocated Druid processes

In certain instances, Druid processes may be colocated on the same host. For example, the Broker and Router may share the same server. Other colocated processes include the Historical and MiddleManager or the Coordinator and Overlord. When you have colocated processes, specify druid.emitter.prometheus.port separately for each process on each host. For example, even if the Broker and Router share the same host, the Broker runtime properties and the Router runtime properties each need to list druid.emitter.prometheus.port, and the port value for both must be different.

Override properties for Peon Tasks

Peon tasks are created dynamically by middle managers and have dynamic host and port addresses. Since the exporter strategy allows Prometheus to read only from a fixed address, it cannot be used for peon tasks. So, these tasks need to be configured to use pushgateway strategy to push metrics from Druid to prometheus gateway.

If this emitter is configured to use exporter strategy globally, some of the above configurations need to be overridden in the middle manager so that spawned peon tasks can still use the pushgateway strategy.

#
# Override global prometheus emitter configuration for peon tasks to use `pushgateway` strategy.
# Other configurations can also be overridden by adding `druid.indexer.fork.property.` prefix to above configuration properties.
# 
druid.indexer.fork.property.druid.emitter.prometheus.strategy=pushgateway
druid.indexer.fork.property.druid.emitter.prometheus.pushGatewayAddress=http://<push-gateway-address>

Metric names

All metric names and labels are reformatted to match Prometheus standards.

  • For names: all characters which are not alphanumeric, underscores, or colons (matching [^a-zA-Z_:][^a-zA-Z0-9_:]*) are replaced with _
  • For labels: all characters which are not alphanumeric or underscores (matching [^a-zA-Z0-9_][^a-zA-Z0-9_]*) are replaced with _

Metric mapping

Each metric to be collected by Prometheus must specify a type, one of [timer, counter, guage]. Prometheus Emitter expects this mapping to be provided as a JSON file. Additionally, this mapping specifies which dimensions should be included for each metric. Prometheus expects histogram timers to use Seconds as the base unit. Timers which do not use seconds as a base unit can use the conversionFactor to set the base time unit. If the user does not specify their own JSON file, a default mapping is used. All metrics are expected to be mapped. Metrics which are not mapped will not be tracked.

Prometheus metric path is organized using the following schema:

<druid metric name> : { 
  "dimensions" : <dimension list>, 
  "type" : <timer|counter|gauge>, 
  "conversionFactor": <conversionFactor>, 
  "help" : <help text>
}

For example:

"query/time" : { 
  "dimensions" : ["dataSource", "type"],
  "type" : "timer",
  "conversionFactor": 1000.0,
  "help": "Seconds taken to complete a query."
}

For metrics which are emitted from multiple services with different dimensions, the metric name is prefixed with the service name. For example:

"coordinator-segment/count" : { "dimensions" : ["dataSource"], "type" : "gauge" },
"historical-segment/count" : { "dimensions" : ["dataSource", "tier", "priority"], "type" : "gauge" }

For most use cases, the default mapping is sufficient.

โ† Aliyun OSSkubernetes โ†’
  • Introduction
  • Configuration
    • Ports for colocated Druid processes
    • Override properties for Peon Tasks
    • Metric names
    • Metric mapping

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