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Ingestion overview

Loading data in Druid is called ingestion or indexing. When you ingest data into Druid, Druid reads the data from your source system and stores it in data files called segments. In general, segment files contain a few million rows each.

For most ingestion methods, the Druid MiddleManager processes or the Indexer processes load your source data. The sole exception is Hadoop-based ingestion, which uses a Hadoop MapReduce job on YARN.

During ingestion, Druid creates segments and stores them in deep storage. Historical nodes load the segments into memory to respond to queries. For streaming ingestion, the Middle Managers and indexers can respond to queries in real-time with arriving data. See the Storage design section of the Druid design documentation for more information.

This topic introduces streaming and batch ingestion methods. The following topics describe ingestion concepts and information that apply to all ingestion methods:

  • Druid schema model introduces concepts of datasources, primary timestamp, dimensions, and metrics.
  • Data rollup describes rollup as a concept and provides suggestions to maximize the benefits of rollup.
  • Partitioning describes time chunk and secondary partitioning in Druid.
  • Ingestion spec reference provides a reference for the configuration options in the ingestion spec.

For additional information about concepts and configurations that are unique to each ingestion method, see the topic for the ingestion method.

Ingestion methods

The tables below list Druid's most common data ingestion methods, along with comparisons to help you choose the best one for your situation. Each ingestion method supports its own set of source systems to pull from. For details about how each method works, as well as configuration properties specific to that method, check out its documentation page.


There are two available options for streaming ingestion. Streaming ingestion is controlled by a continuously-running supervisor.

Supervisor typekafkakinesis
How it worksDruid reads directly from Apache Kafka.Druid reads directly from Amazon Kinesis.
Can ingest late data?Yes.Yes.
Exactly-once guarantees?Yes.Yes.


There are three available options for batch ingestion. Batch ingestion jobs are associated with a controller task that runs for the duration of the job.

MethodNative batchSQLHadoop-based
Controller task typeindex_parallelquery_controllerindex_hadoop
How you submit itSend an index_parallel spec to the Tasks API.Send an INSERT or REPLACE statement to the SQL task API.Send an index_hadoop spec to the Tasks API.
ParallelismUsing subtasks, if maxNumConcurrentSubTasks is greater than 1.Using query_worker subtasks.Using YARN.
Fault toleranceWorkers automatically relaunched upon failure. Controller task failure leads to job failure.Controller or worker task failure leads to job failure.YARN containers automatically relaunched upon failure. Controller task failure leads to job failure.
Can append?Yes.Yes (INSERT).No.
Can overwrite?Yes.Yes (REPLACE).Yes.
External dependenciesNone.None.Hadoop cluster.
Input sourcesAny inputSource.Any inputSource (using EXTERN) or Druid datasource (using FROM).Any Hadoop FileSystem or Druid datasource.
Input formatsAny inputFormat.Any inputFormat.Any Hadoop InputFormat.
Secondary partitioning optionsDynamic, hash-based, and range-based partitioning methods are available. See partitionsSpec for details.Range partitioning (CLUSTERED BY).Hash-based or range-based partitioning via partitionsSpec.
Rollup modesPerfect if forceGuaranteedRollup = true in the tuningConfig.Always perfect.Always perfect.