JSON-based batch
This page describes JSON-based batch ingestion using ingestion specs. For SQL-based batch ingestion using the druid-multi-stage-query
extension, see SQL-based ingestion. Refer to the ingestion methods table to determine which ingestion method is right for you.
Apache Druid supports the following types of JSON-based batch indexing tasks:
- Parallel task indexing (
index_parallel
) that can run multiple indexing tasks concurrently. Parallel task works well for production ingestion tasks. - Simple task indexing (
index
) that run a single indexing task at a time. Simple task indexing is suitable for development and test environments.
This topic covers the configuration for index_parallel
ingestion specs.
For related information on batch indexing, see:
- Batch ingestion method comparison table for a comparison of batch ingestion methods.
- Tutorial: Loading a file for a tutorial on JSON-based batch ingestion.
- Input sources for possible input sources.
- Source input formats for possible input formats.
Submit an indexing task
To run either kind of JSON-based batch indexing task, you can:
- Use the Load Data UI in the web console to define and submit an ingestion spec.
- Define an ingestion spec in JSON based upon the examples and reference topics for batch indexing. Then POST the ingestion spec to the Tasks API endpoint,
/druid/indexer/v1/task
, the Overlord service. Alternatively, you can use the indexing script included with Druid atbin/post-index-task
.
Parallel task indexing
The parallel task type index_parallel
is a task for multi-threaded batch indexing. Parallel task indexing only relies on Druid resources. It 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 the individual portions of data.
Druid issues the worker tasks to the Overlord. The Overlord schedules and runs the workers on MiddleManagers or Indexers. After a worker task successfully processes the assigned input portion, it reports the resulting segment list to the Supervisor task.
The Supervisor task periodically checks the status of worker tasks. If a task fails, the Supervisor retries the 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 partitionsSpec
for more details.
Parallel tasks require the following:
- A splittable
inputSource
in theioConfig
. For a list of supported splittable input formats, see Splittable input sources. - The
maxNumConcurrentSubTasks
greater than 1 in thetuningConfig
. Otherwise tasks run sequentially. Theindex_parallel
task reads each input file one by one and creates segments by itself.
Supported compression formats
JSON-based batch ingestion supports the following compression formats:
bz2
gz
xz
zip
sz
(Snappy)zst
(ZSTD)
Implementation considerations
This section covers implementation details to consider when you implement parallel task ingestion.
Volume control for worker tasks
You can control the amount of input data each worker task processes using different configurations depending on the phase in parallel ingestion.
See partitionsSpec
for details about how partitioning affects data volume for tasks.
For the tasks that read data from the inputSource
, you can set the Split hint spec in the tuningConfig
.
For the task that merge shuffled segments, you can set the totalNumMergeTasks
in the tuningConfig
.
Number of running tasks
The maxNumConcurrentSubTasks
in the tuningConfig
determines the number of concurrent worker tasks that run in parallel. The Supervisor task checks the number of current running worker tasks and creates more if it's smaller than maxNumConcurrentSubTasks
regardless of the number of available task slots. This may affect to other ingestion performance. See Capacity planning section for more details.
Replacing or appending data
By default, JSON-based batch ingestion replaces all data in the intervals in your granularitySpec
for any segment that it writes to. If you want to add to the segment instead, set the appendToExisting
flag in the ioConfig
. JSON-based batch ingestion only replaces data in segments where it actively adds data. If there are segments in the intervals for your granularitySpec
that don't have data from a task, they remain unchanged. If any existing segments partially overlap with the intervals in the granularitySpec
, the portion of those segments outside the interval for the new spec remain visible.
You can also perform concurrent append and replace tasks. For more information, see Concurrent append and replace
Fully replacing existing segments using tombstones
This feature is still experimental.
You can set dropExisting
flag in the ioConfig
to true if you want the ingestion task to replace all existing segments that start and end within the intervals for your granularitySpec
. This applies whether or not the new data covers all existing segments. dropExisting
only applies when appendToExisting
is false and the granularitySpec
contains an interval
.
The following examples demonstrate when to set the dropExisting
property to true in the ioConfig
:
Consider an existing segment with an interval of 2020-01-01 to 2021-01-01 and YEAR
segmentGranularity
. You want to overwrite the whole interval of 2020-01-01 to 2021-01-01 with new data using the finer segmentGranularity of MONTH
. If the replacement data does not have a record within every months from 2020-01-01 to 2021-01-01 Druid cannot drop the original YEAR
segment even if it does include all the replacement data. Set dropExisting
to true in this case to replace the original segment at YEAR
segmentGranularity
since you no longer need it.
Imagine you want to re-ingest or overwrite a datasource and the new data does not contain some time intervals that exist in the datasource. For example, a datasource contains the following data at MONTH
segmentGranularity
:
- January: 1 record
- February: 10 records
- March: 10 records
You want to re-ingest and overwrite with new data as follows:
- January: 0 records
- February: 10 records
- March: 9 records
Unless you set dropExisting
to true, the result after ingestion with overwrite using the same MONTH
segmentGranularity
would be:
- January: 1 record
- February: 10 records
- March: 9 records
This may not be what it is expected since the new data has 0 records for January. Set dropExisting
to true to replace the unneeded January segment with a tombstone.
Parallel indexing example
The following example illustrates the configuration for a parallel indexing task.
Click to view the example
{
"type": "index_parallel",
"spec": {
"dataSchema": {
"dataSource": "wikipedia_parallel_index_test",
"timestampSpec": {
"column": "timestamp"
},
"dimensionsSpec": {
"dimensions": [
"country",
"page",
"language",
"user",
"unpatrolled",
"newPage",
"robot",
"anonymous",
"namespace",
"continent",
"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",
"partitionsSpec": {
"type": "single_dim",
"partitionDimension": "country",
"targetRowsPerSegment": 5000000
},
"maxNumConcurrentSubTasks": 2
}
}
}
Parallel indexing configuration
The following table defines the primary sections of the input spec:
Property | Description | Required |
---|---|---|
type | The task type. For parallel task indexing, set the value to index_parallel . | yes |
id | The task ID. If omitted, Druid generates the task ID using the task type, data source name, interval, and date-time stamp. | no |
spec | The ingestion spec that defines the data schema, IO config, and tuning config. | yes |
context | Context to specify various task configuration parameters. See Task context parameters for more details. | no |
dataSchema
This field is required. In general, it defines the way that Druid stores your data: the primary timestamp column, the dimensions, metrics, and any transformations. For an overview, see Ingestion Spec DataSchema.
When defining the granularitySpec
for index parallel, consider the defining intervals
explicitly if you know the time range of the data. This way locking failure happens faster and Druid won't accidentally replace data outside the interval range some rows contain unexpected timestamps. The reasoning is as follows:
- If you explicitly define
intervals
, JSON-based batch ingestion locks all intervals specified when it starts up. Problems with locking become evident quickly when multiple ingestion or indexing tasks try to obtain a lock on the same interval. For example, if a Kafka ingestion task tries to obtain a lock on a locked interval causing the ingestion task fail. Furthermore, if there are rows outside the specified intervals, Druid drops them, avoiding conflict with unexpected intervals. - If you don't define
intervals
, JSON-based batch ingestion locks each interval when the interval is discovered. In this case, if the task overlaps with a higher-priority task, issues with conflicting locks occur later in the ingestion process. If the source data includes rows with unexpected timestamps, they may caused unexpected locking of intervals.
ioConfig
The following table lists the properties of a ioConfig
object:
Property | Description | Default | Required |
---|---|---|---|
type | The task type. Set to the value to index_parallel . | none | yes |
inputFormat | inputFormat to specify how to parse input data. | none | yes |
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 aninterval , then the ingestion task replaces all existing segments fully contained by the specified interval when the task publishes new segments. If ingestion fails, Druid doesn't change any existing segments. In the case of misconfiguration where either appendToExisting is true or interval isn't specified in granularitySpec , Druid doesn't replace any segments even if dropExisting is true . WARNING: this feature is still experimental. | false | no |
tuningConfig
The tuningConfig
is optional. Druid uses default parameters if tuningConfig
is not specified.
The following table lists the properties of a tuningConfig
object:
Property | Description | Default | Required |
---|---|---|---|
type | The task type. Set the value toindex_parallel . | none | yes |
maxRowsInMemory | Determines when Druid should perform intermediate persists to disk. Normally you don't need to set this. Depending on the nature of your data, if rows are short in terms of bytes. For example, you may not want to store a million rows in memory. In this case, set this value. | 1000000 | no |
maxBytesInMemory | Use to determine when Druid should perform intermediate persists to disk. Normally Druid computes this internally and you don't 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 decrease. Tasks fail when the sum of bytes of all intermediary persisted artifacts exceeds maxBytesInMemory . | 1/6 of max JVM memory | no |
maxColumnsToMerge | Limit of the number of segments to merge in a single phase when merging segments for publishing. This limit affects the total number of columns present in a set of segments to merge. If the limit is exceeded, segment merging occurs in multiple phases. Druid merges at least 2 segments per phase, regardless of this setting. | -1 (unlimited) | no |
maxTotalRows | Deprecated. Use partitionsSpec instead. Total number of rows in segments waiting to be pushed. Used to determine 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. | null | no |
splitHintSpec | Hint to control the amount of data that each first phase task reads. Druid may ignore the hint depending on the implementation of the input source. See Split hint spec for more details. | size-based split hint spec | no |
partitionsSpec | Defines how to partition data in each timeChunk, see 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. | null | no |
indexSpecForIntermediatePersists | Defines segment storage format options to use at indexing time for intermediate persisted temporary segments. You can use this configuration to disable dimension/metric compression on intermediate segments to reduce memory required for final merging. However, if you disable compression on intermediate segments, page cache use my increase while intermediate segments are used before Druid merges them to the final published segment published. See IndexSpec for possible values. | same as indexSpec | no |
maxPendingPersists | Maximum number of pending persists that remain not started. If a new intermediate persist exceeds this limit, ingestion blocks 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 perfect rollup. The perfect rollup optimizes the total size of generated segments and querying time but increases indexing time. If true, specify intervals in the granularitySpec and use either hashed or single_dim for the partitionsSpec . You cannot use this flag in conjunction with appendToExisting of IOConfig . For more details, see Segment pushing modes. | false | no |
reportParseExceptions | If true, Druid throws exceptions encountered during parsing and halts ingestion. If false, Druid skips unparseable rows and fields. | false | no |
pushTimeout | Milliseconds to wait to push segments. Must be >= 0, where 0 means to wait forever. | 0 | no |
segmentWriteOutMediumFactory | Segment write-out medium to use when creating segments. See SegmentWriteOutMediumFactory. | If not specified, uses the value from druid.peon.defaultSegmentWriteOutMediumFactory.type | no |
maxNumConcurrentSubTasks | Maximum number of worker tasks that can be run in parallel at the same time. The supervisor task spawns worker tasks up to maxNumConcurrentSubTasks regardless of the current available task slots. If this value is 1, the supervisor task processes data ingestion on its own instead of spawning worker tasks. If this value is set to too large, the supervisor may create too many worker tasks that block other ingestion tasks. See Capacity planning for more details. | 1 | no |
maxRetry | Maximum number of retries on task failures. | 3 | 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 when forceGuaranteedRollup is true. | 100 | no |
totalNumMergeTasks | Total number of tasks that merge segments in the merge phase when partitionsSpec is set to hashed or single_dim . | 10 | no |
taskStatusCheckPeriodMs | Polling period in milliseconds to check running task statuses. | 1000 | no |
chatHandlerTimeout | Timeout for reporting the pushed segments in worker tasks. | PT10S | no |
chatHandlerNumRetries | Retries for reporting the pushed segments in worker tasks. | 5 | no |
awaitSegmentAvailabilityTimeoutMillis | Milliseconds to wait for the newly indexed segments to become available for query after ingestion completes. If <= 0 , no wait occurs. If > 0 , the task waits for the Coordinator to indicate that the new segments are available for querying. If the timeout expires, the task exits as successful, but the segments are not confirmed as available for query. | Long | no (default = 0) |
Split Hint Spec
The split hint spec is used to help the supervisor task divide input sources. Each worker task processes a single input division. You can control the amount of data each worker task reads during the first phase.
Size-based Split Hint Spec
The size-based split hint spec affects all splittable input sources except for the HTTP input source and SQL input source.
Property | Description | Default | Required |
---|---|---|---|
type | Set the value to maxSize . | none | yes |
maxSplitSize | Maximum number of bytes of input files to process in a single subtask. If a single file is larger than the limit, Druid processes the file alone in a single subtask. Druid does not split files across tasks. One subtask will not process more files than maxNumFiles even when their total size is smaller than maxSplitSize . Human-readable format is supported. | 1GiB | no |
maxNumFiles | Maximum number of input files to process in a single subtask. This limit avoids task failures when the ingestion spec is too long. There are two known limits on the max size of serialized ingestion spec: the max ZNode size in ZooKeeper (jute.maxbuffer ) and the max packet size in MySQL (max_allowed_packet ). These limits can cause ingestion tasks fail if the serialized ingestion spec size hits one of them. One subtask will not process more data than maxSplitSize even when the total number of files is smaller than maxNumFiles . | 1000 | no |
Segments Split Hint Spec
The segments split hint spec is used only for DruidInputSource
.
Property | Description | Default | Required |
---|---|---|---|
type | Set the value to segments . | none | yes |
maxInputSegmentBytesPerTask | Maximum number of bytes of input segments to process in a single subtask. If a single segment is larger than this number, Druid processes the segment alone in a single subtask. Druid never splits input segments across tasks. A single subtask will not process more segments than maxNumSegments even when their total size is smaller than maxInputSegmentBytesPerTask . Human-readable format is supported. | 1GiB | no |
maxNumSegments | Maximum number of input segments to process in a single subtask. This limit avoids failures due to the the ingestion spec being too long. There are two known limits on the max size of serialized ingestion spec: the max ZNode size in ZooKeeper (jute.maxbuffer ) and the max packet size in MySQL (max_allowed_packet ). These limits can make ingestion tasks fail when the serialized ingestion spec size hits one of them. A single subtask will not process more data than maxInputSegmentBytesPerTask even when the total number of segments is smaller than maxNumSegments . | 1000 | no |
partitionsSpec
The primary partition for Druid is time. You can define a secondary partitioning method in the partitions spec. Use the partitionsSpec
type that applies for your rollup method.
For perfect rollup, you can use:
hashed
partitioning based on the hash value of specified dimensions for each rowsingle_dim
based on ranges of values for a single dimensionrange
based on ranges of values of multiple dimensions.
For best-effort rollup, use dynamic
.
For an overview, see Partitioning.
The partitionsSpec
types have different characteristics.
PartitionsSpec | Ingestion speed | Partitioning method | Supported rollup mode | Secondary partition pruning at query time |
---|---|---|---|---|
dynamic | Fastest | Dynamic partitioning based on the number of rows in a segment. | Best-effort rollup | N/A |
hashed | Moderate | Multiple dimension hash-based partitioning may reduce both your datasource size and query latency by improving data locality. See Partitioning 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.Note that partitionDimensions must be set at ingestion time to enable secondary partition pruning at query time. |
single_dim | Slower | Single dimension range partitioning may reduce your datasource size and query latency by improving data locality. See Partitioning 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. |
range | Slowest | Multiple dimension range partitioning may reduce your datasource size and query latency by improving data locality. See Partitioning 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 partitionDimensions values within each segment, given a query including a filter on the first of the partitionDimensions , the broker can pick up only the segments holding the rows satisfying the filter on the first partition dimension for query processing. |
Dynamic partitioning
Property | Description | Default | Required |
---|---|---|---|
type | Set the value to 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 spawning multiple worker tasks (type single_phase_sub_task
), each of which creates segments.
How the worker task creates segments:
- Whenever the number of rows in the current segment exceeds
maxRowsPerSegment
. - When the total number of rows in all segments across all time chunks reaches to
maxTotalRows
. At this point the task pushes all segments created so far to the deep storage and creates new ones.
Hash-based partitioning
Property | Description | Default | Required |
---|---|---|---|
type | Set the value to hashed . | none | yes |
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 |
partitionDimensions | The dimensions to partition on. Leave blank to select all dimensions. | null | no |
partitionFunction | A function to compute hash of partition dimensions. See Hash partition function | murmur3_32_abs | no |
The Parallel task with hash-based partitioning is similar to MapReduce.
The task runs in up to three 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.
name | description |
---|---|
murmur3_32_abs | Applies an absolute value function to the result of murmur3_32 . |
Single-dimension range partitioning
Single dimension range partitioning is not supported in the sequential mode of the index_parallel
task type.
Range partitioning has several benefits related to storage footprint and query performance.
The Parallel task will use one subtask when you set maxNumConcurrentSubTasks
to 1.
When you use this technique to partition your data, segment sizes may be unequally distributed if the data in your partitionDimension
is also unequally distributed. Therefore, to avoid imbalance in data layout, review the distribution of values in your source data before deciding on a partitioning strategy.
Range partitioning is not possible on multi-value dimensions. If the provided
partitionDimension
is multi-value, your ingestion job will report an error.
Property | Description | Default | Required |
---|---|---|---|
type | Set the value to single_dim . | none | yes |
partitionDimension | The dimension to partition on. Only rows with a single dimension value are allowed. | none | yes |
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 |
maxRowsPerSegment | Soft max for the number of rows to include in a partition. | none | either this or targetRowsPerSegment |
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 |
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.
Multi-dimension range partitioning
Multi-dimension range partitioning is not supported in the sequential mode of the index_parallel
task type.
Range partitioning has several benefits related to storage footprint and query performance. Multi-dimension range partitioning improves over single-dimension range partitioning by allowing Druid to distribute segment sizes more evenly, and to prune on more dimensions.
Range partitioning is not possible on multi-value dimensions. If one of the provided
partitionDimensions
is multi-value, your ingestion job will report an error.
Property | Description | Default | Required |
---|---|---|---|
type | Set the value to range . | none | yes |
partitionDimensions | An array of dimensions to partition on. Order the dimensions from most frequently queried to least frequently queried. For best results, limit your number of dimensions to between three and five dimensions. | none | yes |
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 |
maxRowsPerSegment | Soft max for the number of rows to include in a partition. | none | either this or targetRowsPerSegment |
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 |
Benefits of range partitioning
Range partitioning, either single_dim
or range
, has several benefits:
- Lower storage footprint due to combining similar data into the same segments, which improves compressibility.
- Better query performance due to Broker-level segment pruning, which removes segments from consideration when they cannot possibly contain data matching the query filter.
For Broker-level segment pruning to be effective, you must include partition dimensions in the WHERE
clause. Each
partition dimension can participate in pruning if the prior partition dimensions (those to its left) are also
participating, and if the query uses filters that support pruning.
Filters that support pruning include:
- Equality on string literals, like
x = 'foo'
andx IN ('foo', 'bar')
wherex
is a string. - Comparison between string columns and string literals, like
x < 'foo'
or other comparisons involving<
,>
,<=
, or>=
.
For example, if you configure the following range
partitioning during ingestion:
"partitionsSpec": {
"type": "range",
"partitionDimensions": ["countryName", "cityName"],
"targetRowsPerSegment": 5000000
}
Then, filters like WHERE countryName = 'United States'
or WHERE countryName = 'United States' AND cityName = 'New York'
can make use of pruning. However, WHERE cityName = 'New York'
cannot make use of pruning, because countryName is not
involved. The clause WHERE cityName LIKE 'New%'
cannot make use of pruning either, because LIKE filters do not
support pruning.
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.
Click to view the response
{
"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": {
"type": "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,
"dropExisting": false
},
"tuningConfig": {
"type": "index_parallel",
"partitionsSpec": {
"type": "dynamic"
},
"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
}
}
},
"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.
Segment pushing modes
While ingesting data using the parallel task indexing, Druid creates segments from the input data and pushes them. For segment pushing, the parallel task index supports the following segment pushing modes based upon your type of rollup:
- Bulk pushing mode: Used for perfect rollup. Druid pushes every segment at the very end of the index task. Until then, Druid stores created segments in memory and local storage of the service running the index task. To enable bulk pushing mode, set
forceGuaranteedRollup
totrue
in your tuning config. You cannot use bulk pushing withappendToExisting
in your IOConfig. - Incremental pushing mode: Used for best-effort rollup. Druid pushes segments are incrementally during the course of the indexing task. The index task collects data and stores created segments in the memory and disks of the services running the task until the total number of collected rows exceeds
maxTotalRows
. At that point the index task immediately pushes all segments created up until that moment, cleans up pushed segments, and continues to ingest the remaining data.
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.
Splittable input sources
Use the inputSource
object to define the location where your index can read data. Only the native parallel task and simple task support the input source.
For details on available input sources see:
- S3 input source (
s3
) reads data from Amazon S3 storage. - Google Cloud Storage input source (
gs
) reads data from Google Cloud Storage. - Azure input source (
azure
) reads data from Azure Blob Storage and Azure Data Lake. - HDFS input source (
hdfs
) reads data from HDFS storage. - HTTP input Source (
http
) reads data from HTTP servers. - Inline input Source reads data you paste into the web console.
- Local input Source (
local
) reads data from local storage. - Druid input Source (
druid
) reads data from a Druid datasource. - SQL input Source (
sql
) reads data from a RDBMS source.
For information on how to combine input sources, see Combining input source.
segmentWriteOutMediumFactory
Property | Type | Description | Required |
---|---|---|---|
type | String | See Additional Peon Configuration: SegmentWriteOutMediumFactory for explanation and available options. | yes |