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`](#partitionsspec).
See each `partitionsSpec` for more details.
To use this task, the [`inputSource`](#input-sources) in the `ioConfig` should be _splittable_ and `maxNumConcurrentSubTasks` should be set to larger than 1 in the `tuningConfig`.
|type|The task type, this should always be `index_parallel`.|yes|
|id|The task ID. If this is not explicitly specified, Druid generates the task ID using task type, data source name, interval, and date-time stamp. |no|
|spec|The ingestion spec including the data schema, IOConfig, and TuningConfig. See below for more details. |yes|
|context|Context containing various task configuration parameters. See below for more details.|no|
|appendToExisting|Creates segments as additional shards of the latest version, effectively appending to the segment set instead of replacing it. This means that you can append new segments to any datasource regardless of its original partitioning scheme. You must use the `dynamic` partitioning type for the appended segments. If you specify a different partitioning type, the task fails with an error.|false|no|
|dropExisting|If `true` and `appendToExisting` is `false` and the `granularitySpec` contains an`interval`, then the ingestion task drops (mark unused) all existing segments fully contained by the specified `interval` when the task publishes new segments. If ingestion fails, Druid does not drop or mark unused any segments. In the case of misconfiguration where either `appendToExisting` is `true` or `interval` is not specified in `granularitySpec`, Druid does not drop any segments even if `dropExisting` is `true`. WARNING: this functionality is still in beta and can result in temporary data unavailability for data within the specified `interval`.|false|no|
|maxRowsInMemory|Used in determining when intermediate persists to disk should occur. Normally user does not need to set this, but depending on the nature of data, if rows are short in terms of bytes, user may not want to store a million rows in memory and this value should be set.|1000000|no|
|maxBytesInMemory|Used in determining when intermediate persists to disk should occur. Normally this is computed internally and user does not need to set it. This value represents number of bytes to aggregate in heap memory before persisting. This is based on a rough estimate of memory usage and not actual usage. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists). Note that `maxBytesInMemory` also includes heap usage of artifacts created from intermediary persists. This means that after every persist, the amount of `maxBytesInMemory` until next persist will decreases, and task will fail when the sum of bytes of all intermediary persisted artifacts exceeds `maxBytesInMemory`.|1/6 of max JVM memory|no|
|maxColumnsToMerge|A parameter that limits how many segments can be merged in a single phase when merging segments for publishing. This limit is imposed on the total number of columns present in a set of segments being merged. If the limit is exceeded, segment merging will occur in multiple phases. At least 2 segments will be merged in a single phase, regardless of this setting.|-1 (unlimited)|no|
|maxTotalRows|Deprecated. Use `partitionsSpec` instead. Total number of rows in segments waiting for being pushed. Used in determining when intermediate pushing should occur.|20000000|no|
|numShards|Deprecated. Use `partitionsSpec` instead. Directly specify the number of shards to create when using a `hashed``partitionsSpec`. If this is specified and `intervals` is specified in the `granularitySpec`, the index task can skip the determine intervals/partitions pass through the data. `numShards` cannot be specified if `maxRowsPerSegment` is set.|null|no|
|splitHintSpec|Used to give a hint to control the amount of data that each first phase task reads. This hint could be ignored depending on the implementation of the input source. See [Split hint spec](#split-hint-spec) for more details.|size-based split hint spec|no|
|partitionsSpec|Defines how to partition data in each timeChunk, see [PartitionsSpec](#partitionsspec)|`dynamic` if `forceGuaranteedRollup` = false, `hashed` or `single_dim` if `forceGuaranteedRollup` = true|no|
|indexSpec|Defines segment storage format options to be used at indexing time, see [IndexSpec](ingestion-spec.md#indexspec)|null|no|
|indexSpecForIntermediatePersists|Defines segment storage format options to be used at indexing time for intermediate persisted temporary segments. this can be used to disable dimension/metric compression on intermediate segments to reduce memory required for final merging. however, disabling compression on intermediate segments might increase page cache use while they are used before getting merged into final segment published, see [IndexSpec](ingestion-spec.md#indexspec) for possible values.|same as indexSpec|no|
|maxPendingPersists|Maximum number of persists that can be pending but not started. If this limit would be exceeded by a new intermediate persist, ingestion will block until the currently-running persist finishes. Maximum heap memory usage for indexing scales with maxRowsInMemory * (2 + maxPendingPersists).|0 (meaning one persist can be running concurrently with ingestion, and none can be queued up)|no|
|forceGuaranteedRollup|Forces guaranteeing the [perfect rollup](rollup.md). The perfect rollup optimizes the total size of generated segments and querying time while indexing time will be increased. If this is set to true, `intervals` in `granularitySpec` must be set and `hashed` or `single_dim` must be used for `partitionsSpec`. This flag cannot be used with `appendToExisting` of IOConfig. For more details, see the below __Segment pushing modes__ section.|false|no|
|reportParseExceptions|If true, exceptions encountered during parsing will be thrown and will halt ingestion; if false, unparseable rows and fields will be skipped.|false|no|
|pushTimeout|Milliseconds to wait for pushing segments. It must be >= 0, where 0 means to wait forever.|0|no|
|segmentWriteOutMediumFactory|Segment write-out medium to use when creating segments. See [SegmentWriteOutMediumFactory](#segmentwriteoutmediumfactory).|Not specified, the value from `druid.peon.defaultSegmentWriteOutMediumFactory.type` is used|no|
|maxNumConcurrentSubTasks|Maximum number of worker tasks which can be run in parallel at the same time. The supervisor task would spawn worker tasks up to `maxNumConcurrentSubTasks` regardless of the current available task slots. If this value is set to 1, the supervisor task processes data ingestion on its own instead of spawning worker tasks. If this value is set to too large, too many worker tasks can be created which might block other ingestion. Check [Capacity Planning](#capacity-planning) for more details.|1|no|
|maxNumSegmentsToMerge|Max limit for the number of segments that a single task can merge at the same time in the second phase. Used only `forceGuaranteedRollup` is set.|100|no|
|awaitSegmentAvailabilityTimeoutMillis|Long|Milliseconds to wait for the newly indexed segments to become available for query after ingestion completes. If `<= 0`, no wait will occur. If `> 0`, the task will wait for the Coordinator to indicate that the new segments are available for querying. If the timeout expires, the task will exit as successful, but the segments were not confirmed to have become available for query.|no (default = 0)|
|maxSplitSize|Maximum number of bytes of input files to process in a single subtask. If a single file is larger than this number, it will be processed by itself in a single subtask (Files are never split across tasks yet). Note that one subtask will not process more files than `maxNumFiles` even when their total size is smaller than `maxSplitSize`. [Human-readable format](../configuration/human-readable-byte.md) is supported.|1GiB|no|
|maxNumFiles|Maximum number of input files to process in a single subtask. This limit is to avoid task failures when the ingestion spec is too long. There are two known limits on the max size of serialized ingestion spec, i.e., the max ZNode size in ZooKeeper (`jute.maxbuffer`) and the max packet size in MySQL (`max_allowed_packet`). These can make ingestion tasks fail if the serialized ingestion spec size hits one of them. Note that one subtask will not process more data than `maxSplitSize` even when the total number of files is smaller than `maxNumFiles`.|1000|no|
The segments split hint spec is used only for [`DruidInputSource`](#druid-input-source) (and legacy [`IngestSegmentFirehose`](#ingestsegmentfirehose)).
|maxInputSegmentBytesPerTask|Maximum number of bytes of input segments to process in a single subtask. If a single segment is larger than this number, it will be processed by itself in a single subtask (input segments are never split across tasks). Note that one subtask will not process more segments than `maxNumSegments` even when their total size is smaller than `maxInputSegmentBytesPerTask`. [Human-readable format](../configuration/human-readable-byte.md) is supported.|1GiB|no|
|maxNumSegments|Maximum number of input segments to process in a single subtask. This limit is to avoid task failures when the ingestion spec is too long. There are two known limits on the max size of serialized ingestion spec, i.e., the max ZNode size in ZooKeeper (`jute.maxbuffer`) and the max packet size in MySQL (`max_allowed_packet`). These can make ingestion tasks fail if the serialized ingestion spec size hits one of them. Note that one subtask will not process more data than `maxInputSegmentBytesPerTask` even when the total number of segments is smaller than `maxNumSegments`.|1000|no|
| `hashed` | Moderate | Multiple dimension [hash-based partitioning](#hash-based-partitioning) may reduce both your datasource size and query latency by improving data locality. See [Partitioning](./partitioning.md) for more details. | Perfect rollup | The broker can use the partition information to prune segments early to speed up queries. Since the broker knows how to hash `partitionDimensions` values to locate a segment, given a query including a filter on all the `partitionDimensions`, the broker can pick up only the segments holding the rows satisfying the filter on `partitionDimensions` for query processing.<br/><br/>Note that `partitionDimensions` must be set at ingestion time to enable secondary partition pruning at query time.|
| `single_dim` | Slowest | Single dimension [range partitioning](#single-dimension-range-partitioning) may reduce your datasource size and query latency by improving data locality. See [Partitioning](./partitioning.md) for more details. | Perfect rollup | The broker can use the partition information to prune segments early to speed up queries. Since the broker knows the range of `partitionDimension` values in each segment, given a query including a filter on the `partitionDimension`, the broker can pick up only the segments holding the rows satisfying the filter on `partitionDimension` for query processing. |
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
|property|description|default|required?|
|--------|-----------|-------|---------|
|type|This should always be `dynamic`|none|yes|
|maxRowsPerSegment|Used in sharding. Determines how many rows are in each segment.|5000000|no|
|maxTotalRows|Total number of rows across all segments waiting for being pushed. Used in determining when intermediate segment push should occur.|20000000|no|
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.
|numShards|Directly specify the number of shards to create. If this is specified and `intervals` is specified in the `granularitySpec`, the index task can skip the determine intervals/partitions pass through the data. This property and `targetRowsPerSegment` cannot both be set.|none|no|
|targetRowsPerSegment|A target row count for each partition. If `numShards` is left unspecified, the Parallel task will determine a partition count automatically such that each partition has a row count close to the target, assuming evenly distributed keys in the input data. A target per-segment row count of 5 million is used if both `numShards` and `targetRowsPerSegment` are null. |null (or 5,000,000 if both `numShards` and `targetRowsPerSegment` are null)|no|
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.
|name|description|
|----|-----------|
|`murmur3_32_abs`|Applies an absolute value function to the result of [`murmur3_32`](https://guava.dev/releases/16.0/api/docs/com/google/common/hash/Hashing.html#murmur3_32()).|
|targetRowsPerSegment|Target number of rows to include in a partition, should be a number that targets segments of 500MB\~1GB.|none|either this or `maxRowsPerSegment`|
|assumeGrouped|Assume that input data has already been grouped on time and dimensions. Ingestion will run faster, but may choose sub-optimal partitions if this assumption is violated.|false|no|
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](../design/middlemanager.md) or the [indexer](../design/indexer.md).
- 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.
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.
|id|The task ID. If this is not explicitly specified, Druid generates the task ID using task type, data source name, interval, and date-time stamp. |no|
|spec|The ingestion spec including the data schema, IOConfig, and TuningConfig. See below for more details. |yes|
|context|Context containing various task configuration parameters. See below for more details.|no|
|appendToExisting|Creates segments as additional shards of the latest version, effectively appending to the segment set instead of replacing it. This means that you can append new segments to any datasource regardless of its original partitioning scheme. You must use the `dynamic` partitioning type for the appended segments. If you specify a different partitioning type, the task fails with an error.|false|no|
|dropExisting|If `true` and `appendToExisting` is `false` and the `granularitySpec` contains an`interval`, then the ingestion task drops (mark unused) all existing segments fully contained by the specified `interval` when the task publishes new segments. If ingestion fails, Druid does not drop or mark unused any segments. In the case of misconfiguration where either `appendToExisting` is `true` or `interval` is not specified in `granularitySpec`, Druid does not drop any segments even if `dropExisting` is `true`. WARNING: this functionality is still in beta and can result in temporary data unavailability for data within the specified `interval`.|false|no|
|maxRowsInMemory|Used in determining when intermediate persists to disk should occur. Normally user does not need to set this, but depending on the nature of data, if rows are short in terms of bytes, user may not want to store a million rows in memory and this value should be set.|1000000|no|
|maxBytesInMemory|Used in determining when intermediate persists to disk should occur. Normally this is computed internally and user does not need to set it. This value represents number of bytes to aggregate in heap memory before persisting. This is based on a rough estimate of memory usage and not actual usage. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists). Note that `maxBytesInMemory` also includes heap usage of artifacts created from intermediary persists. This means that after every persist, the amount of `maxBytesInMemory` until next persist will decreases, and task will fail when the sum of bytes of all intermediary persisted artifacts exceeds `maxBytesInMemory`.|1/6 of max JVM memory|no|
|maxTotalRows|Deprecated. Use `partitionsSpec` instead. Total number of rows in segments waiting for being pushed. Used in determining when intermediate pushing should occur.|20000000|no|
|numShards|Deprecated. Use `partitionsSpec` instead. Directly specify the number of shards to create. If this is specified and `intervals` is specified in the `granularitySpec`, the index task can skip the determine intervals/partitions pass through the data. `numShards` cannot be specified if `maxRowsPerSegment` is set.|null|no|
|partitionDimensions|Deprecated. Use `partitionsSpec` instead. The dimensions to partition on. Leave blank to select all dimensions. Only used with `forceGuaranteedRollup` = true, will be ignored otherwise.|null|no|
|partitionsSpec|Defines how to partition data in each timeChunk, see [PartitionsSpec](#partitionsspec)|`dynamic` if `forceGuaranteedRollup` = false, `hashed` if `forceGuaranteedRollup` = true|no|
|indexSpec|Defines segment storage format options to be used at indexing time, see [IndexSpec](ingestion-spec.md#indexspec)|null|no|
|indexSpecForIntermediatePersists|Defines segment storage format options to be used at indexing time for intermediate persisted temporary segments. This can be used to disable dimension/metric compression on intermediate segments to reduce memory required for final merging. However, disabling compression on intermediate segments might increase page cache use while they are used before getting merged into final segment published, see [IndexSpec](ingestion-spec.md#indexspec) for possible values.|same as indexSpec|no|
|maxPendingPersists|Maximum number of persists that can be pending but not started. If this limit would be exceeded by a new intermediate persist, ingestion will block until the currently-running persist finishes. Maximum heap memory usage for indexing scales with maxRowsInMemory * (2 + maxPendingPersists).|0 (meaning one persist can be running concurrently with ingestion, and none can be queued up)|no|
|forceGuaranteedRollup|Forces guaranteeing the [perfect rollup](rollup.md). The perfect rollup optimizes the total size of generated segments and querying time while indexing time will be increased. If this is set to true, the index task will read the entire input data twice: one for finding the optimal number of partitions per time chunk and one for generating segments. Note that the result segments would be hash-partitioned. This flag cannot be used with `appendToExisting` of IOConfig. For more details, see the below __Segment pushing modes__ section.|false|no|
|reportParseExceptions|DEPRECATED. If true, exceptions encountered during parsing will be thrown and will halt ingestion; if false, unparseable rows and fields will be skipped. Setting `reportParseExceptions` to true will override existing configurations for `maxParseExceptions` and `maxSavedParseExceptions`, setting `maxParseExceptions` to 0 and limiting `maxSavedParseExceptions` to no more than 1.|false|no|
|segmentWriteOutMediumFactory|Segment write-out medium to use when creating segments. See [SegmentWriteOutMediumFactory](#segmentwriteoutmediumfactory).|Not specified, the value from `druid.peon.defaultSegmentWriteOutMediumFactory.type` is used|no|
|logParseExceptions|If true, log an error message when a parsing exception occurs, containing information about the row where the error occurred.|false|no|
|maxParseExceptions|The maximum number of parse exceptions that can occur before the task halts ingestion and fails. Overridden if `reportParseExceptions` is set.|unlimited|no|
|maxSavedParseExceptions|When a parse exception occurs, Druid can keep track of the most recent parse exceptions. "maxSavedParseExceptions" limits how many exception instances will be saved. These saved exceptions will be made available after the task finishes in the [task completion report](tasks.md#task-reports). Overridden if `reportParseExceptions` is set.|0|no|
|numShards|Directly specify the number of shards to create. If this is specified and `intervals` is specified in the `granularitySpec`, the index task can skip the determine intervals/partitions pass through the data. `numShards` cannot be specified if `maxRowsPerSegment` is set.|null|no|
|type|String|See [Additional Peon Configuration: SegmentWriteOutMediumFactory](../configuration/index.md#segmentwriteoutmediumfactory) for explanation and available options.|yes|
|prefixes|JSON array of URI prefixes for the locations of S3 objects to be ingested. Empty objects starting with one of the given prefixes will be skipped.|None|`uris` or `prefixes` or `objects` must be set|
|properties|Properties Object for overriding the default S3 configuration. See below for more information.|None|No (defaults will be used if not given)
|accessKeyId|The [Password Provider](../operations/password-provider.md) or plain text string of this S3 InputSource's access key|None|yes if secretAccessKey is given|
|secretAccessKey|The [Password Provider](../operations/password-provider.md) or plain text string of this S3 InputSource's secret key|None|yes if accessKeyId is given|
|assumeRoleArn|AWS ARN of the role to assume [see](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_request.html). **assumeRoleArn** can be used either with the ingestion spec AWS credentials or with the default S3 credentials|None|no|
|assumeRoleExternalId|A unique identifier that might be required when you assume a role in another account [see](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_request.html)|None|no|
**Note :** *If accessKeyId and secretAccessKey are not given, the default [S3 credentials provider chain](../development/extensions-core/s3.md#s3-authentication-methods) is used.*
> You need to include the [`druid-google-extensions`](../development/extensions-core/google.md) 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
|prefixes|JSON array of URI prefixes for the locations of Google Cloud Storage objects to be ingested. Empty objects starting with one of the given prefixes will be skipped.|None|`uris` or `prefixes` or `objects` must be set|
The Azure input source reads objects directly from Azure Blob store or Azure Data Lake sources. You can
specify objects as a list of file URI strings or prefixes. You can split the Azure input source for use with [Parallel task](#parallel-task) indexing and each worker task reads one chunk of the split data.
|uris|JSON array of URIs where the Azure objects to be ingested are located, in the form "azure://\<container>/\<path-to-file\>"|None|`uris` or `prefixes` or `objects` must be set|
|prefixes|JSON array of URI prefixes for the locations of Azure objects to ingest, in the form "azure://\<container>/\<prefix\>". Empty objects starting with one of the given prefixes are skipped.|None|`uris` or `prefixes` or `objects` must be set|
|objects|JSON array of Azure objects to ingest.|None|`uris` or `prefixes` or `objects` must be set|
|paths|HDFS paths. Can be either a JSON array or comma-separated string of paths. Wildcards like `*` are supported in these paths. Empty files located under one of the given paths will be skipped.|None|yes|
You can also ingest from other storage using the HDFS input source if the HDFS client supports that storage.
However, if you want to ingest from cloud storage, consider using the service-specific input source for your data storage.
If you want to use a non-hdfs protocol with the HDFS input source, include the protocol
in `druid.ingestion.hdfs.allowedProtocols`. See [HDFS input source security configuration](../configuration/index.md#hdfs-input-source) for more details.
The HTTP input source is to support reading files directly from remote sites via HTTP.
> **NOTE:** Ingestion tasks run under the operating system account that runs the Druid processes, for example the Indexer, Middle Manager, and Peon. This means any user who can submit an ingestion task can specify an `HTTPInputSource` at any location where the Druid process has permissions. For example, using `HTTPInputSource`, a console user has access to internal network locations where the they would be denied access otherwise.
> **WARNING:** `HTTPInputSource` is not limited to the HTTP or HTTPS protocols. It uses the Java `URI` class that supports HTTP, HTTPS, FTP, file, and jar protocols by default. This means you should never run Druid under the `root` account, because a user can use the file protocol to access any files on the local disk.
For more information about security best practices, see [Security overview](../operations/security-overview.md#best-practices).
|httpAuthenticationUsername|Username to use for authentication with specified URIs. Can be optionally used if the URIs specified in the spec require a Basic Authentication Header.|None|no|
|httpAuthenticationPassword|PasswordProvider to use with specified URIs. Can be optionally used if the URIs specified in the spec require a Basic Authentication Header.|None|no|
You can only use protocols listed in the `druid.ingestion.http.allowedProtocols` property as HTTP input sources.
The `http` and `https` protocols are allowed by default. See [HTTP input source security configuration](../configuration/index.md#http-input-source) for more details.
|filter|A wildcard filter for files. See [here](http://commons.apache.org/proper/commons-io/apidocs/org/apache/commons/io/filefilter/WildcardFileFilter) for more information.|yes if `baseDir` is specified|
|baseDir|Directory to search recursively for files to be ingested. Empty files under the `baseDir` will be skipped.|At least one of `baseDir` or `files` should be specified|
|files|File paths to ingest. Some files can be ignored to avoid ingesting duplicate files if they are located under the specified `baseDir`. Empty files will be skipped.|At least one of `baseDir` or `files` should be specified|
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](#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.
|database|Specifies the database connection details. The database type corresponds to the extension that supplies the `connectorConfig` support. The specified extension must be loaded into Druid:<br/><br/><ul><li>[mysql-metadata-storage](../development/extensions-core/mysql.md) for `mysql`</li><li> [postgresql-metadata-storage](../development/extensions-core/postgresql.md) extension for `postgresql`.</li></ul><br/><br/>You can selectively allow JDBC properties in `connectURI`. See [JDBC connections security config](../configuration/index.md#jdbc-connections-to-external-databases) for more details.|Yes|
|foldCase|Toggle case folding of database column names. This may be enabled in cases where the database returns case insensitive column names in query results.|No|
|sqls|List of SQL queries where each SQL query would retrieve the data to be indexed.|Yes|
An example SqlInputSource spec is shown below:
```json
...
"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`.
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](#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.
|property|description|required?|
|--------|-----------|---------|
|type|This should be "combining".|Yes|
|delegates|List of _splittable_ InputSources to read data from.|Yes|
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.
|property|description|default|required?|
|--------|-----------|-------|---------|
|type|This should be `static-s3`.|None|yes|
|uris|JSON array of URIs where s3 files to be ingested are located.|None|`uris` or `prefixes` must be set|
|prefixes|JSON array of URI prefixes for the locations of s3 files to be ingested.|None|`uris` or `prefixes` must be set|
|maxCacheCapacityBytes|Maximum size of the cache space in bytes. 0 means disabling cache. Cached files are not removed until the ingestion task completes.|1073741824|no|
|maxFetchCapacityBytes|Maximum size of the fetch space in bytes. 0 means disabling prefetch. Prefetched files are removed immediately once they are read.|1073741824|no|
|prefetchTriggerBytes|Threshold to trigger prefetching s3 objects.|maxFetchCapacityBytes / 2|no|
|fetchTimeout|Timeout for fetching an s3 object.|60000|no|
|maxFetchRetry|Maximum retry for fetching an s3 object.|3|no|
#### StaticGoogleBlobStoreFirehose
> You need to include the [`druid-google-extensions`](../development/extensions-core/google.md) 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](#parallel-task).
Since each split represents an object in this firehose, each worker task of `index_parallel` will read an object.
Sample spec:
```json
"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.
|property|description|default|required?|
|--------|-----------|-------|---------|
|type|This should be `static-google-blobstore`.|None|yes|
|blobs|JSON array of Google Blobs.|None|yes|
|maxCacheCapacityBytes|Maximum size of the cache space in bytes. 0 means disabling cache. Cached files are not removed until the ingestion task completes.|1073741824|no|
|maxFetchCapacityBytes|Maximum size of the fetch space in bytes. 0 means disabling prefetch. Prefetched files are removed immediately once they are read.|1073741824|no|
|prefetchTriggerBytes|Threshold to trigger prefetching Google Blobs.|maxFetchCapacityBytes / 2|no|
|fetchTimeout|Timeout for fetching a Google Blob.|60000|no|
|maxFetchRetry|Maximum retry for fetching a Google Blob.|3|no|
Google Blobs:
|property|description|default|required?|
|--------|-----------|-------|---------|
|bucket|Name of the Google Cloud bucket|None|yes|
|path|The path where data is located.|None|yes|
### HDFSFirehose
> You need to include the [`druid-hdfs-storage`](../development/extensions-core/hdfs.md) 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](#parallel-task).
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.
|Property|Description|Default|
|--------|-----------|-------|
|type|This should be `hdfs`.|none (required)|
|paths|HDFS paths. Can be either a JSON array or comma-separated string of paths. Wildcards like `*` are supported in these paths.|none (required)|
|maxCacheCapacityBytes|Maximum size of the cache space in bytes. 0 means disabling cache. Cached files are not removed until the ingestion task completes.|1073741824|
|maxFetchCapacityBytes|Maximum size of the fetch space in bytes. 0 means disabling prefetch. Prefetched files are removed immediately once they are read.|1073741824|
|prefetchTriggerBytes|Threshold to trigger prefetching files.|maxFetchCapacityBytes / 2|
|fetchTimeout|Timeout for fetching each file.|60000|
|maxFetchRetry|Maximum number of retries for fetching each file.|3|
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](native-batch.md#parallel-task).
Since each split represents a file in this Firehose, each worker task of `index_parallel` will read a file.
|filter|A wildcard filter for files. See [here](http://commons.apache.org/proper/commons-io/apidocs/org/apache/commons/io/filefilter/WildcardFileFilter) for more information.|yes|
You can only use protocols listed in the `druid.ingestion.http.allowedProtocols` property as HTTP firehose input sources.
The `http` and `https` protocols are allowed by default. See [HTTP firehose security configuration](../configuration/index.md#http-input-source) for more details.
|maxCacheCapacityBytes|Maximum size of the cache space in bytes. 0 means disabling cache. Cached files are not removed until the ingestion task completes.|1073741824|
|maxFetchCapacityBytes|Maximum size of the fetch space in bytes. 0 means disabling prefetch. Prefetched files are removed immediately once they are read.|1073741824|
|prefetchTriggerBytes|Threshold to trigger prefetching HTTP objects.|maxFetchCapacityBytes / 2|
|fetchTimeout|Timeout for fetching an HTTP object.|60000|
|maxFetchRetry|Maximum retries for fetching an HTTP object.|3|
<aname="segment-firehose"></a>
### 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](native-batch.md#parallel-task).
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:
```json
{
"type": "ingestSegment",
"dataSource": "wikipedia",
"interval": "2013-01-01/2013-01-02"
}
```
|property|description|required?|
|--------|-----------|---------|
|type|This should be "ingestSegment".|yes|
|dataSource|A String defining the data source to fetch rows from, very similar to a table in a relational database|yes|
|interval|A String representing the ISO-8601 interval. This defines the time range to fetch the data over.|yes|
|dimensions|The list of dimensions to select. If left empty, no dimensions are returned. If left null or not defined, all dimensions are returned. |no|
|metrics|The list of metrics to select. If left empty, no metrics are returned. If left null or not defined, all metrics are selected.|no|
|maxInputSegmentBytesPerTask|Deprecated. Use [Segments Split Hint Spec](#segments-split-hint-spec) instead. When used with the native parallel index task, the maximum number of bytes of input segments to process in a single task. If a single segment is larger than this number, it will be processed by itself in a single task (input segments are never split across tasks). Defaults to 150MB.|no|
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.
|database|Specifies the database connection details. The database type corresponds to the extension that supplies the `connectorConfig` support. The specified extension must be loaded into Druid:<br/><br/><ul><li>[mysql-metadata-storage](../development/extensions-core/mysql.md) for `mysql`</li><li> [postgresql-metadata-storage](../development/extensions-core/postgresql.md) extension for `postgresql`.</li></ul><br/><br/>You can selectively allow JDBC properties in `connectURI`. See [JDBC connections security config](../configuration/index.md#jdbc-connections-to-external-databases) for more details.||Yes|
|maxCacheCapacityBytes|Maximum size of the cache space in bytes. 0 means disabling cache. Cached files are not removed until the ingestion task completes.|1073741824|No|
|maxFetchCapacityBytes|Maximum size of the fetch space in bytes. 0 means disabling prefetch. Prefetched files are removed immediately once they are read.|1073741824|No|
|prefetchTriggerBytes|Threshold to trigger prefetching SQL result objects.|maxFetchCapacityBytes / 2|No|
|fetchTimeout|Timeout for fetching the result set.|60000|No|
|foldCase|Toggle case folding of database column names. This may be enabled in cases where the database returns case insensitive column names in query results.|false|No|
|sqls|List of SQL queries where each SQL query would retrieve the data to be indexed.||Yes|
#### Database
|property|description|default|required?|
|--------|-----------|-------|---------|
|type|The type of database to query. Valid values are `mysql` and `postgresql`_||Yes|
|connectorConfig|Specify the database connection properties via `connectURI`, `user` and `password`||Yes|
### 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:
```json
{
"type": "inline",
"data": "0,values,formatted\n1,as,CSV"
}
```
|property|description|required?|
|--------|-----------|---------|
|type|This should be "inline".|yes|
|data|Inlined data to ingest.|yes|
### CombiningFirehose
This Firehose can be used to combine and merge data from a list of different Firehoses.
