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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
  • Get to know Query view
  • Unnesting arrays
  • Jupyter Notebook tutorials
  • Docker for tutorials
  • JDBC connector

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
    • Array 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

Docker for Jupyter Notebook tutorials

Apache Druid provides a custom Jupyter container that contains the prerequisites for all Jupyter-based Druid tutorials, as well as all of the tutorials themselves. You can run the Jupyter container, as well as containers for Druid and Apache Kafka, using the Docker Compose file provided in the Druid GitHub repository.

You can run the following combination of applications:

  • Jupyter only
  • Jupyter and Druid
  • Jupyter, Druid, and Kafka

Prerequisites

Jupyter in Docker requires that you have Docker and Docker Compose. We recommend installing these through Docker Desktop.

Launch the Docker containers

You run Docker Compose to launch Jupyter and optionally Druid or Kafka. Docker Compose references the configuration in docker-compose.yaml. Running Druid in Docker also requires the environment file, which sets the configuration properties for the Druid services. To get started, download both docker-compose.yaml and environment from tutorial-jupyter-docker.zip.

Alternatively, you can clone the Apache Druid repo and access the files in druid/examples/quickstart/jupyter-notebooks/docker-jupyter.

Start only the Jupyter container

If you already have Druid running locally, you can run only the Jupyter container to complete the tutorials. In the same directory as docker-compose.yaml, start the application:

docker compose --profile jupyter up -d

The Docker Compose file assigns 8889 for the Jupyter port. You can override the port number by setting the JUPYTER_PORT environment variable before starting the Docker application.

Start Jupyter and Druid

Running Druid in Docker requires the environment file as well as an environment variable named DRUID_VERSION, which determines the version of Druid to use. The Druid version references the Docker tag to pull from the Apache Druid Docker Hub.

In the same directory as docker-compose.yaml and environment, start the application:

DRUID_VERSION=26.0.0 docker compose --profile druid-jupyter up -d

Start Jupyter, Druid, and Kafka

Running Druid in Docker requires the environment file as well as the DRUID_VERSION environment variable.

In the same directory as docker-compose.yaml and environment, start the application:

DRUID_VERSION=26.0.0 docker compose --profile all-services up -d

Update image from Docker Hub

If you already have a local cache of the Jupyter image, you can update the image before running the application using the following command:

docker compose pull jupyter

Use locally built image

The default Docker Compose file pulls the custom Jupyter Notebook image from a third party Docker Hub. If you prefer to build the image locally from the official source, do the following:

  1. Clone the Apache Druid repository.
  2. Navigate to examples/quickstart/jupyter-notebooks/docker-jupyter.
  3. Start the services using -f docker-compose-local.yaml in the docker compose command. For example:
DRUID_VERSION=26.0.0 docker compose --profile all-services -f docker-compose-local.yaml up -d

Access Jupyter-based tutorials

The following steps show you how to access the Jupyter notebook tutorials from the Docker container. At startup, Docker creates and mounts a volume to persist data from the container to your local machine. This way you can save your work completed within the Docker container.

  1. Navigate to the notebooks at http://localhost:8889.

    If you set JUPYTER_PORT to another port number, replace 8889 with the value of the Jupyter port.

  2. Select a tutorial. If you don't plan to save your changes, you can use the notebook directly as is. Otherwise, continue to the next step.

  3. Optional: To save a local copy of your tutorial work, select File > Save as... from the navigation menu. Then enter work/<notebook name>.ipynb. If the notebook still displays as read only, you may need to refresh the page in your browser. Access the saved files in the notebooks folder in your local working directory.

View the Druid web console

To access the Druid web console in Docker, go to http://localhost:8888/unified-console.html. Use the web console to view datasources and ingestion tasks that you create in the tutorials.

Stop Docker containers

Shut down the Docker application using the following command:

docker compose down -v

Tutorial setup without using Docker

To use the Jupyter Notebook-based tutorials without using Docker, do the following:

  1. Clone the Apache Druid repo, or download the tutorials as well as the Python client for Druid.

  2. Install the prerequisite Python packages with the following commands:

    # Install requests
    pip install requests
    
    # Install JupyterLab
    pip install jupyterlab
    
    # Install Jupyter Notebook
    pip install notebook
    

    Individual notebooks may list additional packages you need to install to complete the tutorial.

  3. In your Druid source repo, install druidapi with the following commands:

    cd examples/quickstart/jupyter-notebooks/druidapi
    pip install .
    
  4. Start Jupyter, in the same directory as the tutorials, using either JupyterLab or Jupyter Notebook:

    # Start JupyterLab on port 3001
    jupyter lab --port 3001
    
    # Start Jupyter Notebook on port 3001
    jupyter notebook --port 3001
    
  5. Start Druid. You can use the Quickstart (local) instance. The tutorials assume that you are using the quickstart, so no authentication or authorization is expected unless explicitly mentioned.

    If you contribute to Druid, and work with Druid integration tests, you can use a test cluster. Assume you have an environment variable, DRUID_DEV, which identifies your Druid source repo.

    cd $DRUID_DEV
    ./it.sh build
    ./it.sh image
    ./it.sh up <category>
    

    Replace <category> with one of the available integration test categories. See the integration test README.md for details.

You should now be able to access and complete the tutorials.

Learn more

See the following topics for more information:

  • Jupyter Notebook tutorials for the available Jupyter Notebook-based tutorials for Druid
  • Tutorial: Run with Docker for running Druid from a Docker container
โ† Jupyter Notebook tutorialsJDBC connector โ†’
  • Prerequisites
  • Launch the Docker containers
    • Start only the Jupyter container
    • Start Jupyter and Druid
    • Start Jupyter, Druid, and Kafka
    • Update image from Docker Hub
    • Use locally built image
  • Access Jupyter-based tutorials
  • View the Druid web console
  • Stop Docker containers
  • Tutorial setup without using Docker
  • Learn more

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