Native batch ingestion

Apache Druid currently has two types of native batch indexing tasks, index_parallel which can run multiple tasks in parallel, and index which will run a single indexing task. Please refer to our Hadoop-based vs. native batch comparison table for comparisons between Hadoop-based, native batch (simple), and native batch (parallel) ingestion.

To run either kind of native batch indexing task, write an ingestion spec as specified below. Then POST it to the /druid/indexer/v1/task endpoint on the Overlord, or use the bin/post-index-task script included with Druid.

Tutorial

This page contains reference documentation for native batch ingestion. For a walk-through instead, check out the Loading a file tutorial, which demonstrates the “simple” (single-task) mode.

Parallel task

The Parallel task (type index_parallel) is a task for parallel batch indexing. This task only uses Druid’s resource and doesn’t depend on other external systems like Hadoop. The index_parallel task is a supervisor task that orchestrates the whole indexing process. The supervisor task splits the input data and creates worker tasks to process those splits. The created worker tasks are issued to the Overlord so that they can be scheduled and run on MiddleManagers or Indexers. Once a worker task successfully processes the assigned input split, it reports the generated segment list to the supervisor task. The supervisor task periodically checks the status of worker tasks. If one of them fails, it retries the failed task until the number of retries reaches the configured limit. If all worker tasks succeed, it publishes the reported segments at once and finalizes ingestion.

The detailed behavior of the Parallel task is different depending on the partitionsSpec. See each partitionsSpec for more details.

To use this task, the inputSource in the ioConfig should be splittable and maxNumConcurrentSubTasks should be set to larger than 1 in the tuningConfig. Otherwise, this task runs sequentially; the index_parallel task reads each input file one by one and creates segments by itself. The supported splittable input formats for now are:

• s3 reads data from AWS S3 storage.
• gs reads data from Google Cloud Storage.
• azure reads data from Azure Blob Storage.
• hdfs reads data from HDFS storage.
• http reads data from HTTP servers.
• local reads data from local storage.
• druid reads data from a Druid datasource.
• sql reads data from a RDBMS source.

Some other cloud storage types are supported with the legacy firehose. The below firehose types are also splittable. Note that only text formats are supported with the firehose.

Compression formats supported

The supported compression formats for native batch ingestion are bz2, gz, xz, zip, sz (Snappy), and zst (ZSTD).

• static-cloudfiles

You may want to consider the below things:

• You may want to control the amount of input data each worker task processes. This can be controlled using different configurations depending on the phase in parallel ingestion (see partitionsSpec for more details). For the tasks that read data from the inputSource, you can set the Split hint spec in the tuningConfig. For the tasks that merge shuffled segments, you can set the totalNumMergeTasks in the tuningConfig.
• The number of concurrent worker tasks in parallel ingestion is determined by maxNumConcurrentSubTasks in the tuningConfig. The supervisor task checks the number of current running worker tasks and creates more if it’s smaller than maxNumConcurrentSubTasks no matter how many task slots are currently available. This may affect to other ingestion performance. See the below Capacity Planning section for more details.
• By default, batch ingestion replaces all data (in your granularitySpec‘s intervals) in any segment that it writes to. If you’d like to add to the segment instead, set the appendToExisting flag in the ioConfig. Note that it only replaces data in segments where it actively adds data: if there are segments in your granularitySpec‘s intervals that have no data written by this task, they will be left alone. If any existing segments partially overlap with the granularitySpec‘s intervals, the portion of those segments outside the new segments’ intervals will still be visible.

Task syntax

A sample task is shown below:

{
  "type": "index_parallel",
  "spec": {
    "dataSchema": {
      "dataSource": "wikipedia_parallel_index_test",
      "timestampSpec": {
        "column": "timestamp"
      },
      "dimensionsSpec": {
        "dimensions": [
          "page",
          "language",
          "user",
          "unpatrolled",
          "newPage",
          "robot",
          "anonymous",
          "namespace",
          "continent",
          "country",
          "region",
          "city"
        ]
      },
      "metricsSpec": [
        {
          "type": "count",
              "name": "count"
            },
            {
              "type": "doubleSum",
              "name": "added",
              "fieldName": "added"
            },
            {
              "type": "doubleSum",
              "name": "deleted",
              "fieldName": "deleted"
            },
            {
              "type": "doubleSum",
              "name": "delta",
              "fieldName": "delta"
            }
        ],
        "granularitySpec": {
          "segmentGranularity": "DAY",
          "queryGranularity": "second",
          "intervals" : [ "2013-08-31/2013-09-02" ]
        }
    },
    "ioConfig": {
        "type": "index_parallel",
        "inputSource": {
          "type": "local",
          "baseDir": "examples/indexing/",
          "filter": "wikipedia_index_data*"
        },
        "inputFormat": {
          "type": "json"
        }
    },
    "tuningconfig": {
        "type": "index_parallel",
        "maxNumConcurrentSubTasks": 2
    }
  }
}

dataSchema

This field is required.

See Ingestion Spec DataSchema

If you specify intervals explicitly in your dataSchema’s granularitySpec, batch ingestion will lock the full intervals specified when it starts up, and you will learn quickly if the specified interval overlaps with locks held by other tasks (e.g., Kafka ingestion). Otherwise, batch ingestion will lock each interval as it is discovered, so you may only learn that the task overlaps with a higher-priority task later in ingestion. If you specify intervals explicitly, any rows outside the specified intervals will be thrown away. We recommend setting intervals explicitly if you know the time range of the data so that locking failure happens faster, and so that you don’t accidentally replace data outside that range if there’s some stray data with unexpected timestamps.

ioConfig

tuningConfig

The tuningConfig is optional and default parameters will be used if no tuningConfig is specified. See below for more details.

Split Hint Spec

The split hint spec is used to give a hint when the supervisor task creates input splits. Note that each worker task processes a single input split. You can control the amount of data each worker task will read during the first phase.

Size-based Split Hint Spec

The size-based split hint spec is respected by all splittable input sources except for the HTTP input source.

Segments Split Hint Spec

The segments split hint spec is used only for DruidInputSource (and legacy IngestSegmentFirehose).

partitionsSpec

PartitionsSpec is used to describe the secondary partitioning method. You should use different partitionsSpec depending on the rollup mode you want. For perfect rollup, you should use either hashed (partitioning based on the hash of dimensions in each row) or single_dim (based on ranges of a single dimension). For best-effort rollup, you should use dynamic.

The three partitionsSpec types have different characteristics.

The recommended use case for each partitionsSpec is:

• If your data has a uniformly distributed column which is frequently used in your queries, consider using single_dim partitionsSpec to maximize the performance of most of your queries.
• If your data doesn’t have a uniformly distributed column, but is expected to have a high rollup ratio when you roll up with some dimensions, consider using hashed partitionsSpec. It could reduce the size of datasource and query latency by improving data locality.
• If the above two scenarios are not the case or you don’t need to roll up your datasource, consider using dynamic partitionsSpec.

Dynamic partitioning

With the Dynamic partitioning, the parallel index task runs in a single phase: it will spawn multiple worker tasks (type single_phase_sub_task), each of which creates segments. How the worker task creates segments is:

• The task creates a new segment whenever the number of rows in the current segment exceeds maxRowsPerSegment.
• Once the total number of rows in all segments across all time chunks reaches to maxTotalRows, the task pushes all segments created so far to the deep storage and creates new ones.

Hash-based partitioning

The Parallel task with hash-based partitioning is similar to MapReduce. The task runs in up to 3 phases: partial dimension cardinality, partial segment generation and partial segment merge.

• The partial dimension cardinality phase is an optional phase that only runs if numShards is not specified. The Parallel task splits the input data and assigns them to worker tasks based on the split hint spec. Each worker task (type partial_dimension_cardinality) gathers estimates of partitioning dimensions cardinality for each time chunk. The Parallel task will aggregate these estimates from the worker tasks and determine the highest cardinality across all of the time chunks in the input data, dividing this cardinality by targetRowsPerSegment to automatically determine numShards.
• In the partial segment generation phase, just like the Map phase in MapReduce, the Parallel task splits the input data based on the split hint spec and assigns each split to a worker task. Each worker task (type partial_index_generate) reads the assigned split, and partitions rows by the time chunk from segmentGranularity (primary partition key) in the granularitySpec and then by the hash value of partitionDimensions (secondary partition key) in the partitionsSpec. The partitioned data is stored in local storage of the middleManager or the indexer.
• The partial segment merge phase is similar to the Reduce phase in MapReduce. The Parallel task spawns a new set of worker tasks (type partial_index_generic_merge) to merge the partitioned data created in the previous phase. Here, the partitioned data is shuffled based on the time chunk and the hash value of partitionDimensions to be merged; each worker task reads the data falling in the same time chunk and the same hash value from multiple MiddleManager/Indexer processes and merges them to create the final segments. Finally, they push the final segments to the deep storage at once.

Hash partition function

In hash partitioning, the partition function is used to compute hash of partition dimensions. The partition dimension values are first serialized into a byte array as a whole, and then the partition function is applied to compute hash of the byte array. Druid currently supports only one partition function.

Single-dimension range partitioning

Single dimension range partitioning is currently not supported in the sequential mode of the Parallel task. The Parallel task will use one subtask when you set maxNumConcurrentSubTasks to 1.

With single-dim partitioning, the Parallel task runs in 3 phases, i.e., partial dimension distribution, partial segment generation, and partial segment merge. The first phase is to collect some statistics to find the best partitioning and the other 2 phases are to create partial segments and to merge them, respectively, as in hash-based partitioning.

• In the partial dimension distribution phase, the Parallel task splits the input data and assigns them to worker tasks based on the split hint spec. Each worker task (type partial_dimension_distribution) reads the assigned split and builds a histogram for partitionDimension. The Parallel task collects those histograms from worker tasks and finds the best range partitioning based on partitionDimension to evenly distribute rows across partitions. Note that either targetRowsPerSegment or maxRowsPerSegment will be used to find the best partitioning.
• In the partial segment generation phase, the Parallel task spawns new worker tasks (type partial_range_index_generate) to create partitioned data. Each worker task reads a split created as in the previous phase, partitions rows by the time chunk from the segmentGranularity (primary partition key) in the granularitySpec and then by the range partitioning found in the previous phase. The partitioned data is stored in local storage of the middleManager or the indexer.
• In the partial segment merge phase, the parallel index task spawns a new set of worker tasks (type partial_index_generic_merge) to merge the partitioned data created in the previous phase. Here, the partitioned data is shuffled based on the time chunk and the value of partitionDimension; each worker task reads the segments falling in the same partition of the same range from multiple MiddleManager/Indexer processes and merges them to create the final segments. Finally, they push the final segments to the deep storage.

Because the task with single-dimension range partitioning makes two passes over the input in partial dimension distribution and partial segment generation phases, the task may fail if the input changes in between the two passes.

HTTP status endpoints

The supervisor task provides some HTTP endpoints to get running status.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/mode

Returns ‘parallel’ if the indexing task is running in parallel. Otherwise, it returns ‘sequential’.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/phase

Returns the name of the current phase if the task running in the parallel mode.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/progress

Returns the estimated progress of the current phase if the supervisor task is running in the parallel mode.

An example of the result is

{
  "running":10,
  "succeeded":0,
  "failed":0,
  "complete":0,
  "total":10,
  "estimatedExpectedSucceeded":10
}

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtasks/running

Returns the task IDs of running worker tasks, or an empty list if the supervisor task is running in the sequential mode.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtaskspecs

Returns all worker task specs, or an empty list if the supervisor task is running in the sequential mode.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtaskspecs/running

Returns running worker task specs, or an empty list if the supervisor task is running in the sequential mode.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtaskspecs/complete

Returns complete worker task specs, or an empty list if the supervisor task is running in the sequential mode.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtaskspec/{SUB_TASK_SPEC_ID}

Returns the worker task spec of the given id, or HTTP 404 Not Found error if the supervisor task is running in the sequential mode.

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtaskspec/{SUB_TASK_SPEC_ID}/state

Returns the state of the worker task spec of the given id, or HTTP 404 Not Found error if the supervisor task is running in the sequential mode. The returned result contains the worker task spec, a current task status if exists, and task attempt history.

An example of the result is

{
  "spec": {
    "id": "index_parallel_lineitem_2018-04-20T22:12:43.610Z_2",
    "groupId": "index_parallel_lineitem_2018-04-20T22:12:43.610Z",
    "supervisorTaskId": "index_parallel_lineitem_2018-04-20T22:12:43.610Z",
    "context": null,
    "inputSplit": {
      "split": "/path/to/data/lineitem.tbl.5"
    },
    "ingestionSpec": {
      "dataSchema": {
        "dataSource": "lineitem",
        "timestampSpec": {
          "column": "l_shipdate",
          "format": "yyyy-MM-dd"
        },
        "dimensionsSpec": {
          "dimensions": [
            "l_orderkey",
            "l_partkey",
            "l_suppkey",
            "l_linenumber",
            "l_returnflag",
            "l_linestatus",
            "l_shipdate",
            "l_commitdate",
            "l_receiptdate",
            "l_shipinstruct",
            "l_shipmode",
            "l_comment"
          ]
        },
        "metricsSpec": [
          {
            "type": "count",
            "name": "count"
          },
          {
            "type": "longSum",
            "name": "l_quantity",
            "fieldName": "l_quantity",
            "expression": null
          },
          {
            "type": "doubleSum",
            "name": "l_extendedprice",
            "fieldName": "l_extendedprice",
            "expression": null
          },
          {
            "type": "doubleSum",
            "name": "l_discount",
            "fieldName": "l_discount",
            "expression": null
          },
          {
            "type": "doubleSum",
            "name": "l_tax",
            "fieldName": "l_tax",
            "expression": null
          }
        ],
        "granularitySpec": {
          "type": "uniform",
          "segmentGranularity": "YEAR",
          "queryGranularity": {
            "type": "none"
          },
          "rollup": true,
          "intervals": [
            "1980-01-01T00:00:00.000Z/2020-01-01T00:00:00.000Z"
          ]
        },
        "transformSpec": {
          "filter": null,
          "transforms": []
        }
      },
      "ioConfig": {
        "type": "index_parallel",
        "inputSource": {
          "type": "local",
          "baseDir": "/path/to/data/",
          "filter": "lineitem.tbl.5"
        },
        "inputFormat": {
          "format": "tsv",
          "delimiter": "|",
          "columns": [
            "l_orderkey",
            "l_partkey",
            "l_suppkey",
            "l_linenumber",
            "l_quantity",
            "l_extendedprice",
            "l_discount",
            "l_tax",
            "l_returnflag",
            "l_linestatus",
            "l_shipdate",
            "l_commitdate",
            "l_receiptdate",
            "l_shipinstruct",
            "l_shipmode",
            "l_comment"
          ]
        },
        "appendToExisting": false
      },
      "tuningConfig": {
        "type": "index_parallel",
        "maxRowsPerSegment": 5000000,
        "maxRowsInMemory": 1000000,
        "maxTotalRows": 20000000,
        "numShards": null,
        "indexSpec": {
          "bitmap": {
            "type": "roaring"
          },
          "dimensionCompression": "lz4",
          "metricCompression": "lz4",
          "longEncoding": "longs"
        },
        "indexSpecForIntermediatePersists": {
          "bitmap": {
            "type": "roaring"
          },
          "dimensionCompression": "lz4",
          "metricCompression": "lz4",
          "longEncoding": "longs"
        },
        "maxPendingPersists": 0,
        "reportParseExceptions": false,
        "pushTimeout": 0,
        "segmentWriteOutMediumFactory": null,
        "maxNumConcurrentSubTasks": 4,
        "maxRetry": 3,
        "taskStatusCheckPeriodMs": 1000,
        "chatHandlerTimeout": "PT10S",
        "chatHandlerNumRetries": 5,
        "logParseExceptions": false,
        "maxParseExceptions": 2147483647,
        "maxSavedParseExceptions": 0,
        "forceGuaranteedRollup": false,
        "buildV9Directly": true
      }
    }
  },
  "currentStatus": {
    "id": "index_sub_lineitem_2018-04-20T22:16:29.922Z",
    "type": "index_sub",
    "createdTime": "2018-04-20T22:16:29.925Z",
    "queueInsertionTime": "2018-04-20T22:16:29.929Z",
    "statusCode": "RUNNING",
    "duration": -1,
    "location": {
      "host": null,
      "port": -1,
      "tlsPort": -1
    },
    "dataSource": "lineitem",
    "errorMsg": null
  },
  "taskHistory": []
}

• http://{PEON_IP}:{PEON_PORT}/druid/worker/v1/chat/{SUPERVISOR_TASK_ID}/subtaskspec/{SUB_TASK_SPEC_ID}/history

Returns the task attempt history of the worker task spec of the given id, or HTTP 404 Not Found error if the supervisor task is running in the sequential mode.

Capacity planning

The supervisor task can create up to maxNumConcurrentSubTasks worker tasks no matter how many task slots are currently available. As a result, total number of tasks which can be run at the same time is (maxNumConcurrentSubTasks + 1) (including the supervisor task). Please note that this can be even larger than total number of task slots (sum of the capacity of all workers). If maxNumConcurrentSubTasks is larger than n (available task slots), then maxNumConcurrentSubTasks tasks are created by the supervisor task, but only n tasks would be started. Others will wait in the pending state until any running task is finished.

If you are using the Parallel Index Task with stream ingestion together, we would recommend to limit the max capacity for batch ingestion to prevent stream ingestion from being blocked by batch ingestion. Suppose you have t Parallel Index Tasks to run at the same time, but want to limit the max number of tasks for batch ingestion to b. Then, (sum of maxNumConcurrentSubTasks of all Parallel Index Tasks + t (for supervisor tasks)) must be smaller than b.

If you have some tasks of a higher priority than others, you may set their maxNumConcurrentSubTasks to a higher value than lower priority tasks. This may help the higher priority tasks to finish earlier than lower priority tasks by assigning more task slots to them.

Simple task

The simple task (type index) is designed to be used for smaller data sets. The task executes within the indexing service.

Task syntax

A sample task is shown below:

{
  "type" : "index",
  "spec" : {
    "dataSchema" : {
      "dataSource" : "wikipedia",
      "timestampSpec" : {
        "column" : "timestamp",
        "format" : "auto"
      },
      "dimensionsSpec" : {
        "dimensions": ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"],
        "dimensionExclusions" : []
      },
      "metricsSpec" : [
        {
          "type" : "count",
          "name" : "count"
        },
        {
          "type" : "doubleSum",
          "name" : "added",
          "fieldName" : "added"
        },
        {
          "type" : "doubleSum",
          "name" : "deleted",
          "fieldName" : "deleted"
        },
        {
          "type" : "doubleSum",
          "name" : "delta",
          "fieldName" : "delta"
        }
      ],
      "granularitySpec" : {
        "type" : "uniform",
        "segmentGranularity" : "DAY",
        "queryGranularity" : "NONE",
        "intervals" : [ "2013-08-31/2013-09-01" ]
      }
    },
    "ioConfig" : {
      "type" : "index",
      "inputSource" : {
        "type" : "local",
        "baseDir" : "examples/indexing/",
        "filter" : "wikipedia_data.json"
       },
       "inputFormat": {
         "type": "json"
       }
    },
    "tuningConfig" : {
      "type" : "index",
      "maxRowsPerSegment" : 5000000,
      "maxRowsInMemory" : 1000000
    }
  }
}

dataSchema

This field is required.

See the dataSchema section of the ingestion docs for details.

If you do not specify intervals explicitly in your dataSchema’s granularitySpec, the Local Index Task will do an extra pass over the data to determine the range to lock when it starts up. If you specify intervals explicitly, any rows outside the specified intervals will be thrown away. We recommend setting intervals explicitly if you know the time range of the data because it allows the task to skip the extra pass, and so that you don’t accidentally replace data outside that range if there’s some stray data with unexpected timestamps.

ioConfig

tuningConfig

The tuningConfig is optional and default parameters will be used if no tuningConfig is specified. See below for more details.

partitionsSpec

PartitionsSpec is to describe the secondary partitioning method. You should use different partitionsSpec depending on the rollup mode you want. For perfect rollup, you should use hashed.

For best-effort rollup, you should use dynamic.

segmentWriteOutMediumFactory

Segment pushing modes

While ingesting data using the Index task, it creates segments from the input data and pushes them. For segment pushing, the Index task supports two segment pushing modes, i.e., bulk pushing mode and incremental pushing mode for perfect rollup and best-effort rollup, respectively.

In the bulk pushing mode, every segment is pushed at the very end of the index task. Until then, created segments are stored in the memory and local storage of the process running the index task. As a result, this mode might cause a problem due to limited storage capacity, and is not recommended to use in production.

On the contrary, in the incremental pushing mode, segments are incrementally pushed, that is they can be pushed in the middle of the index task. More precisely, the index task collects data and stores created segments in the memory and disks of the process running that task until the total number of collected rows exceeds maxTotalRows. Once it exceeds, the index task immediately pushes all segments created until that moment, cleans all pushed segments up, and continues to ingest remaining data.

To enable bulk pushing mode, forceGuaranteedRollup should be set in the TuningConfig. Note that this option cannot be used with appendToExisting of IOConfig.

Input Sources

The input source is the place to define from where your index task reads data. Only the native Parallel task and Simple task support the input source.

S3 Input Source

You need to include the druid-s3-extensions as an extension to use the S3 input source.

The S3 input source is to support reading objects directly from S3. Objects can be specified either via a list of S3 URI strings or a list of S3 location prefixes, which will attempt to list the contents and ingest all objects contained in the locations. The S3 input source is splittable and can be used by the Parallel task, where each worker task of index_parallel will read one or multiple objects.

Sample specs:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "s3",
        "uris": ["s3://foo/bar/file.json", "s3://bar/foo/file2.json"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "s3",
        "prefixes": ["s3://foo/bar", "s3://bar/foo"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "s3",
        "objects": [
          { "bucket": "foo", "path": "bar/file1.json"},
          { "bucket": "bar", "path": "foo/file2.json"}
        ]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

Note that the S3 input source will skip all empty objects only when prefixes is specified.

S3 Object:

Properties Object:

Note : If accessKeyId and secretAccessKey are not given, the default S3 credentials provider chain is used.

Google Cloud Storage Input Source

You need to include the druid-google-extensions as an extension to use the Google Cloud Storage input source.

The Google Cloud Storage input source is to support reading objects directly from Google Cloud Storage. Objects can be specified as list of Google Cloud Storage URI strings. The Google Cloud Storage input source is splittable and can be used by the Parallel task, where each worker task of index_parallel will read one or multiple objects.

Sample specs:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "google",
        "uris": ["gs://foo/bar/file.json", "gs://bar/foo/file2.json"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "google",
        "prefixes": ["gs://foo/bar", "gs://bar/foo"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "google",
        "objects": [
          { "bucket": "foo", "path": "bar/file1.json"},
          { "bucket": "bar", "path": "foo/file2.json"}
        ]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

Note that the Google Cloud Storage input source will skip all empty objects only when prefixes is specified.

Google Cloud Storage object:

Azure Input Source

You need to include the druid-azure-extensions as an extension to use the Azure input source.

The Azure input source is to support reading objects directly from Azure Blob store. Objects can be specified as list of Azure Blob store URI strings. The Azure input source is splittable and can be used by the Parallel task, where each worker task of index_parallel will read a single object.

Sample specs:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "azure",
        "uris": ["azure://container/prefix1/file.json", "azure://container/prefix2/file2.json"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "azure",
        "prefixes": ["azure://container/prefix1", "azure://container/prefix2"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "azure",
        "objects": [
          { "bucket": "container", "path": "prefix1/file1.json"},
          { "bucket": "container", "path": "prefix2/file2.json"}
        ]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

Note that the Azure input source will skip all empty objects only when prefixes is specified.

Azure Blob object:

HDFS Input Source

You need to include the druid-hdfs-storage as an extension to use the HDFS input source.

The HDFS input source is to support reading files directly from HDFS storage. File paths can be specified as an HDFS URI string or a list of HDFS URI strings. The HDFS input source is splittable and can be used by the Parallel task, where each worker task of index_parallel will read one or multiple files.

Sample specs:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "hdfs",
        "paths": "hdfs://foo/bar/", "hdfs://bar/foo"
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "hdfs",
        "paths": ["hdfs://foo/bar", "hdfs://bar/foo"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "hdfs",
        "paths": "hdfs://foo/bar/file.json", "hdfs://bar/foo/file2.json"
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "hdfs",
        "paths": ["hdfs://foo/bar/file.json", "hdfs://bar/foo/file2.json"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

You can also ingest from cloud storage using the HDFS input source. However, if you want to read from AWS S3 or Google Cloud Storage, consider using the S3 input source or the Google Cloud Storage input source instead.

HTTP Input Source

The HTTP input source is to support reading files directly from remote sites via HTTP. The HTTP input source is splittable and can be used by the Parallel task, where each worker task of index_parallel will read only one file.

Sample specs:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "http",
        "uris": ["http://example.com/uri1", "http://example2.com/uri2"]
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

Example with authentication fields using the DefaultPassword provider (this requires the password to be in the ingestion spec):

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "http",
        "uris": ["http://example.com/uri1", "http://example2.com/uri2"],
        "httpAuthenticationUsername": "username",
        "httpAuthenticationPassword": "password123"
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...

You can also use the other existing Druid PasswordProviders. Here is an example using the EnvironmentVariablePasswordProvider:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "http",
        "uris": ["http://example.com/uri1", "http://example2.com/uri2"],
        "httpAuthenticationUsername": "username",
        "httpAuthenticationPassword": {
          "type": "environment",
          "variable": "HTTP_INPUT_SOURCE_PW"
        }
      },
      "inputFormat": {
        "type": "json"
      },
      ...
    },
...
}

Inline Input Source

The Inline input source can be used to read the data inlined in its own spec. It can be used for demos or for quickly testing out parsing and schema.

Sample spec:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "inline",
        "data": "0,values,formatted\n1,as,CSV"
      },
      "inputFormat": {
        "type": "csv"
      },
      ...
    },
...

Local Input Source

The Local input source is to support reading files directly from local storage, and is mainly intended for proof-of-concept testing. The Local input source is splittable and can be used by the Parallel task, where each worker task of index_parallel will read one or multiple files.

Sample spec:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "local",
        "filter" : "*.csv",
        "baseDir": "/data/directory",
        "files": ["/bar/foo", "/foo/bar"]
      },
      "inputFormat": {
        "type": "csv"
      },
      ...
    },
...

Druid Input Source

The Druid input source is to support reading data directly from existing Druid segments, potentially using a new schema and changing the name, dimensions, metrics, rollup, etc. of the segment. The Druid input source is splittable and can be used by the Parallel task. This input source has a fixed input format for reading from Druid segments; no inputFormat field needs to be specified in the ingestion spec when using this input source.

A minimal example DruidInputSource spec is shown below:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "druid",
        "dataSource": "wikipedia",
        "interval": "2013-01-01/2013-01-02"
      }
      ...
    },
...

The spec above will read all existing dimension and metric columns from the wikipedia datasource, including all rows with a timestamp (the __time column) within the interval 2013-01-01/2013-01-02.

A spec that applies a filter and reads a subset of the original datasource’s columns is shown below.

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "druid",
        "dataSource": "wikipedia",
        "interval": "2013-01-01/2013-01-02",
        "dimensions": [
          "page",
          "user"
        ],
        "metrics": [
          "added"
        ],
        "filter": {
          "type": "selector",
          "dimension": "page",
          "value": "Druid"
        }
      }
      ...
    },
...

This spec above will only return the page, user dimensions and added metric. Only rows where page = Druid will be returned.

SQL Input Source

The SQL input source is used to read data directly from RDBMS. The SQL input source is splittable and can be used by the Parallel task, where each worker task will read from one SQL query from the list of queries. Since this input source has a fixed input format for reading events, no inputFormat field needs to be specified in the ingestion spec when using this input source. Please refer to the Recommended practices section below before using this input source.

An example SqlInputSource spec is shown below:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "sql",
        "database": {
            "type": "mysql",
            "connectorConfig": {
                "connectURI": "jdbc:mysql://host:port/schema",
                "user": "user",
                "password": "password"
            }
        },
        "sqls": ["SELECT * FROM table1 WHERE timestamp BETWEEN '2013-01-01 00:00:00' AND '2013-01-01 11:59:59'", "SELECT * FROM table2 WHERE timestamp BETWEEN '2013-01-01 00:00:00' AND '2013-01-01 11:59:59'"]
      }
    },
...

The spec above will read all events from two separate SQLs for the interval 2013-01-01/2013-01-02. Each of the SQL queries will be run in its own sub-task and thus for the above example, there would be two sub-tasks.

Recommended practices

Compared to the other native batch InputSources, SQL InputSource behaves differently in terms of reading the input data and so it would be helpful to consider the following points before using this InputSource in a production environment:

• During indexing, each sub-task would execute one of the SQL queries and the results are stored locally on disk. The sub-tasks then proceed to read the data from these local input files and generate segments. Presently, there isn’t any restriction on the size of the generated files and this would require the MiddleManagers or Indexers to have sufficient disk capacity based on the volume of data being indexed.

• Filtering the SQL queries based on the intervals specified in the granularitySpec can avoid unwanted data being retrieved and stored locally by the indexing sub-tasks. For example, if the intervals specified in the granularitySpec is ["2013-01-01/2013-01-02"] and the SQL query is SELECT * FROM table1, SqlInputSource will read all the data for table1 based on the query, even though only data between the intervals specified will be indexed into Druid.

• Pagination may be used on the SQL queries to ensure that each query pulls a similar amount of data, thereby improving the efficiency of the sub-tasks.

• Similar to file-based input formats, any updates to existing data will replace the data in segments specific to the intervals specified in the granularitySpec.

Combining Input Source

The Combining input source is used to read data from multiple InputSources. This input source should be only used if all the delegate input sources are splittable and can be used by the Parallel task. This input source will identify the splits from its delegates and each split will be processed by a worker task. Similar to other input sources, this input source supports a single inputFormat. Therefore, please note that delegate input sources requiring an inputFormat must have the same format for input data.

Sample spec:

...
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "combining",
        "delegates" : [
         {
          "type": "local",
          "filter" : "*.csv",
          "baseDir": "/data/directory",
          "files": ["/bar/foo", "/foo/bar"]
         },
         {
          "type": "druid",
          "dataSource": "wikipedia",
          "interval": "2013-01-01/2013-01-02"
         }
        ]
      },
      "inputFormat": {
        "type": "csv"
      },
      ...
    },
...

Firehoses (Deprecated)

Firehoses are deprecated in 0.17.0. It’s highly recommended to use the Input source instead. There are several firehoses readily available in Druid, some are meant for examples, others can be used directly in a production environment.

StaticS3Firehose

You need to include the druid-s3-extensions as an extension to use the StaticS3Firehose.

This firehose ingests events from a predefined list of S3 objects. This firehose is splittable and can be used by the Parallel task. Since each split represents an object in this firehose, each worker task of index_parallel will read an object.

Sample spec:

"firehose" : {
    "type" : "static-s3",
    "uris": ["s3://foo/bar/file.gz", "s3://bar/foo/file2.gz"]
}

This firehose provides caching and prefetching features. In the Simple task, a firehose can be read twice if intervals or shardSpecs are not specified, and, in this case, caching can be useful. Prefetching is preferred when direct scan of objects is slow. Note that prefetching or caching isn’t that useful in the Parallel task.

StaticGoogleBlobStoreFirehose

You need to include the druid-google-extensions as an extension to use the StaticGoogleBlobStoreFirehose.

This firehose ingests events, similar to the StaticS3Firehose, but from an Google Cloud Store.

As with the S3 blobstore, it is assumed to be gzipped if the extension ends in .gz

This firehose is splittable and can be used by the Parallel task. Since each split represents an object in this firehose, each worker task of index_parallel will read an object.

Sample spec:

"firehose" : {
    "type" : "static-google-blobstore",
    "blobs": [
        {
          "bucket": "foo",
          "path": "/path/to/your/file.json"
        },
        {
          "bucket": "bar",
          "path": "/another/path.json"
        }
    ]
}

This firehose provides caching and prefetching features. In the Simple task, a firehose can be read twice if intervals or shardSpecs are not specified, and, in this case, caching can be useful. Prefetching is preferred when direct scan of objects is slow. Note that prefetching or caching isn’t that useful in the Parallel task.

Google Blobs:

HDFSFirehose

You need to include the druid-hdfs-storage as an extension to use the HDFSFirehose.

This firehose ingests events from a predefined list of files from the HDFS storage. This firehose is splittable and can be used by the Parallel task. Since each split represents an HDFS file, each worker task of index_parallel will read files.

Sample spec:

"firehose" : {
    "type" : "hdfs",
    "paths": "/foo/bar,/foo/baz"
}

This firehose provides caching and prefetching features. During native batch indexing, a firehose can be read twice if intervals are not specified, and, in this case, caching can be useful. Prefetching is preferred when direct scanning of files is slow. Note that prefetching or caching isn’t that useful in the Parallel task.

LocalFirehose

This Firehose can be used to read the data from files on local disk, and is mainly intended for proof-of-concept testing, and works with string typed parsers. This Firehose is splittable and can be used by native parallel index tasks. Since each split represents a file in this Firehose, each worker task of index_parallel will read a file. A sample local Firehose spec is shown below:

{
    "type": "local",
    "filter" : "*.csv",
    "baseDir": "/data/directory"
}

HttpFirehose

This Firehose can be used to read the data from remote sites via HTTP, and works with string typed parsers. This Firehose is splittable and can be used by native parallel index tasks. Since each split represents a file in this Firehose, each worker task of index_parallel will read a file. A sample HTTP Firehose spec is shown below:

{
    "type": "http",
    "uris": ["http://example.com/uri1", "http://example2.com/uri2"]
}

The below configurations can be optionally used if the URIs specified in the spec require a Basic Authentication Header. Omitting these fields from your spec will result in HTTP requests with no Basic Authentication Header.

Example with authentication fields using the DefaultPassword provider (this requires the password to be in the ingestion spec):

{
    "type": "http",
    "uris": ["http://example.com/uri1", "http://example2.com/uri2"],
    "httpAuthenticationUsername": "username",
    "httpAuthenticationPassword": "password123"
}

You can also use the other existing Druid PasswordProviders. Here is an example using the EnvironmentVariablePasswordProvider:

{
    "type": "http",
    "uris": ["http://example.com/uri1", "http://example2.com/uri2"],
    "httpAuthenticationUsername": "username",
    "httpAuthenticationPassword": {
        "type": "environment",
        "variable": "HTTP_FIREHOSE_PW"
    }
}

The below configurations can optionally be used for tuning the Firehose performance. Note that prefetching or caching isn’t that useful in the Parallel task.

IngestSegmentFirehose

This Firehose can be used to read the data from existing druid segments, potentially using a new schema and changing the name, dimensions, metrics, rollup, etc. of the segment. This Firehose is splittable and can be used by native parallel index tasks. This firehose will accept any type of parser, but will only utilize the list of dimensions and the timestamp specification. A sample ingest Firehose spec is shown below:

{
    "type": "ingestSegment",
    "dataSource": "wikipedia",
    "interval": "2013-01-01/2013-01-02"
}

SqlFirehose

This Firehose can be used to ingest events residing in an RDBMS. The database connection information is provided as part of the ingestion spec. For each query, the results are fetched locally and indexed. If there are multiple queries from which data needs to be indexed, queries are prefetched in the background, up to maxFetchCapacityBytes bytes. This Firehose is splittable and can be used by native parallel index tasks. This firehose will accept any type of parser, but will only utilize the list of dimensions and the timestamp specification. See the extension documentation for more detailed ingestion examples.

Requires one of the following extensions:

• MySQL Metadata Store.
• PostgreSQL Metadata Store.

{
    "type": "sql",
    "database": {
        "type": "mysql",
        "connectorConfig": {
            "connectURI": "jdbc:mysql://host:port/schema",
            "user": "user",
            "password": "password"
        }
     },
    "sqls": ["SELECT * FROM table1", "SELECT * FROM table2"]
}

Database

InlineFirehose

This Firehose can be used to read the data inlined in its own spec. It can be used for demos or for quickly testing out parsing and schema, and works with string typed parsers. A sample inline Firehose spec is shown below:

{
    "type": "inline",
    "data": "0,values,formatted\n1,as,CSV"
}

CombiningFirehose

This Firehose can be used to combine and merge data from a list of different Firehoses.

{
    "type": "combining",
    "delegates": [ { firehose1 }, { firehose2 }, ... ]
}