* thrust of the fix to allow for the json values to be out of order
The existing problem is that toMap doesn't turn some values into json primitive
values, for example segmentMetadata just has DateTime objects for it's time in
the EventMap, but Alert event converts those into strings when calling toMap.
This creates an issue because when we check the emitted events the mapper
deserializing the string value for dateTime leaves it as a string in the
EventMap. So the question is do we alter the events toMap() to return string/map
version of objects or to make the expected events do a round trip of
eventMap -> string -> eventMap to turn everything into json primitives
* fix issue by making toMap events convert Objects into strings, or maps
* fix linting errors
* use method of using mapper to round trip expected data to make it have same type
as those of the events emitted
* remove unnecessary comment
* Address review comment: add test javadocs
* Fix flaky assertion failure.
Use ConcurrentHashMap instead of HashMap because the producer callback
can trigger concurrently and override the map initialization.
* fixup intellij inspection
* Clean up kafka emitter tests a bit and add more validations.
The test wasn't validating what events were sent, but simply the dropped counters, which
aren't that useful.
Additionally, this module has fewer tests, so folks often run into code coverage issue
in this extension. Hopefully this change helps with that too.
* Change things to feed-based rather than topic-based.
* Another test for shared topic
* Switch to DruidException, add test dependencies and sad path config tests.
* missing test dependency
* minor renames.
* Add more tests - to test unknown events and drop when queue is full
* allow for kafka-emitter to have extra dimensions be set for each event it emits
* fix checktsyle issue in kafkaemitterconfig
* make changes to fix docs, and cleanup copy paste error in #toString()
* undo formatting to markdown table
* add more branches so test passes
* fix checkstyle issue
* Update the group id to org.apache.druid.extensions.contrib for contrib exts.
* Note iceberg and delta lake extensions in extensions.md
* properties and shell backticks
* Update groupId in distribution/pom.xml
* remove delta-lake from dist.
* Add note on downloading extension.
During ingestion, incremental segments are created in memory for the different time chunks and persisted to disk when certain thresholds are reached (max number of rows, max memory, incremental persist period etc). In the case where there are a lot of dimension and metrics (1000+) it was observed that the creation/serialization of incremental segment file format for persistence and persisting the file took a while and it was blocking ingestion of new data. This affected the real-time ingestion. This serialization and persistence can be parallelized across the different time chunks. This update aims to do that.
The patch adds a simple configuration parameter to the ingestion tuning configuration to specify number of persistence threads. The default value is 1 if it not specified which makes it the same as it is today.
This patch bumps Delta Lake Kernel dependency from 3.0.0 to 3.1.0, which released last week - please see https://github.com/delta-io/delta/releases/tag/v3.1.0 for release notes.
There were a few "breaking" API changes in 3.1.0, you can find the rationale for some of those changes here.
Next-up in this extension: add and expose filter predicates.
* something
* test commit
* compilation fix
* more compilation fixes (fixme placeholders)
* Comment out druid-kereberos build since it conflicts with newly added transitive deps from delta-lake
Will need to sort out the dependencies later.
* checkpoint
* remove snapshot schema since we can get schema from the row
* iterator bug fix
* json json json
* sampler flow
* empty impls for read(InputStats) and sample()
* conversion?
* conversion, without timestamp
* Web console changes to show Delta Lake
* Asset bug fix and tile load
* Add missing pieces to input source info, etc.
* fix stuff
* Use a different delta lake asset
* Delta lake extension dependencies
* Cleanup
* Add InputSource, module init and helper code to process delta files.
* Test init
* Checkpoint changes
* Test resources and updates
* some fixes
* move to the correct package
* More tests
* Test cleanup
* TODOs
* Test updates
* requirements and javadocs
* Adjust dependencies
* Update readme
* Bump up version
* fixup typo in deps
* forbidden api and checkstyle checks
* Trim down dependencies
* new lines
* Fixup Intellij inspections.
* Add equals() and hashCode()
* chain splits, intellij inspections
* review comments and todo placeholder
* fix up some docs
* null table path and test dependencies. Fixup broken link.
* run prettify
* Different test; fixes
* Upgrade pyspark and delta-spark to latest (3.5.0 and 3.0.0) and regenerate tests
* yank the old test resource.
* add a couple of sad path tests
* Updates to readme based on latest.
* Version support
* Extract Delta DateTime converstions to DeltaTimeUtils class and add test
* More comprehensive split tests.
* Some test renames.
* Cleanup and update instructions.
* add pruneSchema() optimization for table scans.
* Oops, missed the parquet files.
* Update default table and rename schema constants.
* Test setup and misc changes.
* Add class loader logic as the context class loader is unaware about extension classes
* change some table client creation logic.
* Add hadoop-aws, hadoop-common and related exclusions.
* Remove org.apache.hadoop:hadoop-common
* Apply suggestions from code review
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Add entry to .spelling to fix docs static check
---------
Co-authored-by: abhishekagarwal87 <1477457+abhishekagarwal87@users.noreply.github.com>
Co-authored-by: Laksh Singla <lakshsingla@gmail.com>
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Possibly stabilize intellij-inspections
* remove `integration-tests-ex/cases` from excluded projects from initial build
* enable ErrorProne's `CheckedExceptionNotThrown` to get earlier errors than intellij-inspections
* fix ddsketch pom.xml
* fix spellcheck
* New: Add DDSketch-Druid extension
- Based off of http://www.vldb.org/pvldb/vol12/p2195-masson.pdf and uses
the corresponding https://github.com/DataDog/sketches-java library
- contains tests for post building and using aggregation/post
aggregation.
- New aggregator: `ddSketch`
- New post aggregators: `quantileFromDDSketch` and
`quantilesFromDDSketch`
* Fixing easy CodeQL warnings/errors
* Fixing docs, and dependencies
Also moved aggregator ids to AggregatorUtil and PostAggregatorIds
* Adding more Docs and better null/empty handling for aggregators
* Fixing docs, and pom version
* DDSketch documentation format and wording
### Description
Our Kinesis consumer works by using the [GetRecords API](https://docs.aws.amazon.com/kinesis/latest/APIReference/API_GetRecords.html) in some number of `fetchThreads`, each fetching some number of records (`recordsPerFetch`) and each inserting into a shared buffer that can hold a `recordBufferSize` number of records. The logic is described in our documentation at: https://druid.apache.org/docs/27.0.0/development/extensions-core/kinesis-ingestion/#determine-fetch-settings
There is a problem with the logic that this pr fixes: the memory limits rely on a hard-coded “estimated record size” that is `10 KB` if `deaggregate: false` and `1 MB` if `deaggregate: true`. There have been cases where a supervisor had `deaggregate: true` set even though it wasn’t needed, leading to under-utilization of memory and poor ingestion performance.
Users don’t always know if their records are aggregated or not. Also, even if they could figure it out, it’s better to not have to. So we’d like to eliminate the `deaggregate` parameter, which means we need to do memory management more adaptively based on the actual record sizes.
We take advantage of the fact that GetRecords doesn’t return more than 10MB (https://docs.aws.amazon.com/streams/latest/dev/service-sizes-and-limits.html ):
This pr:
eliminates `recordsPerFetch`, always use the max limit of 10000 records (the default limit if not set)
eliminate `deaggregate`, always have it true
cap `fetchThreads` to ensure that if each fetch returns the max (`10MB`) then we don't exceed our budget (`100MB` or `5% of heap`). In practice this means `fetchThreads` will never be more than `10`. Tasks usually don't have that many processors available to them anyway, so in practice I don't think this will change the number of threads for too many deployments
add `recordBufferSizeBytes` as a bytes-based limit rather than records-based limit for the shared queue. We do know the byte size of kinesis records by at this point. Default should be `100MB` or `10% of heap`, whichever is smaller.
add `maxBytesPerPoll` as a bytes-based limit for how much data we poll from shared buffer at a time. Default is `1000000` bytes.
deprecate `recordBufferSize`, use `recordBufferSizeBytes` instead. Warning is logged if `recordBufferSize` is specified
deprecate `maxRecordsPerPoll`, use `maxBytesPerPoll` instead. Warning is logged if maxRecordsPerPoll` is specified
Fixed issue that when the record buffer is full, the fetchRecords logic throws away the rest of the GetRecords result after `recordBufferOfferTimeout` and starts a new shard iterator. This seems excessively churny. Instead, wait an unbounded amount of time for queue to stop being full. If the queue remains full, we’ll end up right back waiting for it after the restarted fetch.
There was also a call to `newQ::offer` without check in `filterBufferAndResetBackgroundFetch`, which seemed like it could cause data loss. Now checking return value here, and failing if false.
### Release Note
Kinesis ingestion memory tuning config has been greatly simplified, and a more adaptive approach is now taken for the configuration. Here is a summary of the changes made:
eliminates `recordsPerFetch`, always use the max limit of 10000 records (the default limit if not set)
eliminate `deaggregate`, always have it true
cap `fetchThreads` to ensure that if each fetch returns the max (`10MB`) then we don't exceed our budget (`100MB` or `5% of heap`). In practice this means `fetchThreads` will never be more than `10`. Tasks usually don't have that many processors available to them anyway, so in practice I don't think this will change the number of threads for too many deployments
add `recordBufferSizeBytes` as a bytes-based limit rather than records-based limit for the shared queue. We do know the byte size of kinesis records by at this point. Default should be `100MB` or `10% of heap`, whichever is smaller.
add `maxBytesPerPoll` as a bytes-based limit for how much data we poll from shared buffer at a time. Default is `1000000` bytes.
deprecate `recordBufferSize`, use `recordBufferSizeBytes` instead. Warning is logged if `recordBufferSize` is specified
deprecate `maxRecordsPerPoll`, use `maxBytesPerPoll` instead. Warning is logged if maxRecordsPerPoll` is specified
This change intelligently provisions the correct number of threads per scheduled task. 1 for each event type, and 1 for logging the lost events.
This is a change to make this work. But in the future it would be worthwhile to make each task not be greedy and share threads so there isn't a need of a thread per task.
* Add SpectatorHistogram extension
* Clarify documentation
Cleanup comments
* Use ColumnValueSelector directly
so that we support being queried as a Number using longSum or doubleSum aggregators as well as a histogram.
When queried as a Number, we're returning the count of entries in the histogram.
* Apply suggestions from code review
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Fix references
* Fix spelling
* Update docs/development/extensions-contrib/spectator-histogram.md
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
---------
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Fix k8sAndWorker mode in a zookeeper-less environment
* add unit test
* code reformat
* minor refine
* change to inject Provider
* correct style
* bind HttpRemoteTaskRunnerFactory as provider
* change to bind on TaskRunnerFactory
* fix styling
The initial step in optimizing segment metadata was to centralize the construction of datasource schema in the Coordinator (#14985). Subsequently, our goal is to eliminate the requirement for regularly executing queries to obtain segment schema information. This task encompasses addressing both realtime and finalized segments.
This modification specifically addresses the issue with realtime segments. Tasks will now routinely communicate the schema for realtime segments during the segment announcement process. The Coordinator will identify the schema alongside the segment announcement and subsequently update the schema for realtime segments in the metadata cache.
* overhaul DruidPredicateFactory to better handle 3VL
fixes some bugs caused by some limitations of the original design of how DruidPredicateFactory interacts with 3-value logic. The primary impacted area was with how filters on values transformed with expressions or extractionFn which turn non-null values into nulls, which were not possible to be modelled with the 'isNullInputUnknown' method
changes:
* adds DruidObjectPredicate to specialize string, array, and object based predicates instead of using guava Predicate
* DruidPredicateFactory now uses DruidObjectPredicate
* introduces DruidPredicateMatch enum, which all predicates returned from DruidPredicateFactory now use instead of booleans to indicate match. This means DruidLongPredicate, DruidFloatPredicate, DruidDoublePredicate, and the newly added DruidObjectPredicate apply methods all now return DruidPredicateMatch. This allows matchers and indexes
* isNullInputUnknown has been removed from DruidPredicateFactory
* rename, fix test
* adjust
* style
* npe
* more test
* fix default value mode to not match new test
* Reverse lookup fixes and enhancements.
1) Add a "mayIncludeUnknown" parameter to DimFilter#optimize. This is important
because otherwise the reverse-lookup optimization is done improperly when
the "in" filter appears under a "not", and the lookup extractionFn may return
null for some possible values of the filtered column. The "includeUnknown" test
cases in InDimFilterTest illustrate the difference in behavior.
2) Enhance InDimFilter#optimizeLookup to handle "mayIncludeUnknown", and to be able
to do a reverse lookup in a wider variety of cases.
3) Make "unapply" protected in LookupExtractor, and move callers to "unapplyAll".
The main reason is that MapLookupExtractor, a common implementation, lacks a
reverse mapping and therefore does a scan of the map for each call to "unapply".
For performance sake these calls need to be batched.
* Remove optimize call from BloomDimFilter.
* Follow the law.
* Fix tests.
* Fix imports.
* Switch function.
* Fix tests.
* More tests.
* unpin snakeyaml globally, add suppressions and licenses
* pin snakeyaml in the specific modules that require version 1.x, update licenses and owasp suppression
This removes the pin of the Snakeyaml introduced in: https://github.com/apache/druid/pull/14519
After the updates of io.kubernetes.java-client and io.confluent.kafka-clients, the only uses of the Snakeyaml 1.x are:
- in test scope, transitive dependency of jackson-dataformat-yaml🫙2.12.7
- in compile scope in contrib extension druid-cassandra-storage
- in compile scope in it-tests.
With the dependency version un-pinned, io.kubernetes.java-client and io.confluent.kafka-clients bring Snakeyaml versions 2.0 and 2.2, consequently allowing to build a Druid distribution without the contrib-extension and free of vulnerable Snakeyaml versions.
* Allow for kafka emitter producer secrets to be masked in logs instead of being visible
This change will allow for kafka producer config values that should be secrets to not show up in the logs.
This will enhance the security of the people who use the kafka emitter to use this if they want to.
This is opt in and will not affect prior configs for this emitter
* fix checkstyle issue
* change property name
* Clean useless InterruptedException warn in ingestion task log
* test coverage for the code change, manually close the scheduler thread to trigger Interrupt signal
---------
Co-authored-by: Qiong Chen <qiong.chen@shopee.com>
* Optional removal of metrics from Prometheus PushGateway on shutdown
* Make pushGatewayDeleteOnShutdown property nullable
* Add waitForShutdownDelay property
* Fix unit test
* Address PR comments
* Address PR comments
* Add explanation on why it is useful to have deletePushGatewayMetricsOnShutdown
* Fix spelling error
* Fix spelling error
* Fixing failing compaction/parallel index jobs during upgrade due to new actions not available on the overlord.
* Fixing build
* Removing extra space.
* Fixing json getter.
* Review comments.
This patch introduces a param snapshotTime in the iceberg inputsource spec that allows the user to ingest data files associated with the most recent snapshot as of the given time. This helps the user ingest data based on older snapshots by specifying the associated snapshot time.
This patch also upgrades the iceberg core version to 1.4.1
Currently, the redis-cache extension uses Jedis 2.9.0, which was released over seven years ago and is no longer listed in the official support matrix. This patch upgrades it to ensure the compatibility with the recent version of Redis and make future upgrades easier, including:
Upgrade Jedis to v5.0.2, the latest version at this writing, and address the API changes and dependency version mismatch.
Replace mock-jedis with jedis-mock, since the former has not been actively maintained any longer and not compatible with recent versions of Jedis.
In the current design, brokers query both data nodes and tasks to fetch the schema of the segments they serve. The table schema is then constructed by combining the schemas of all segments within a datasource. However, this approach leads to a high number of segment metadata queries during broker startup, resulting in slow startup times and various issues outlined in the design proposal.
To address these challenges, we propose centralizing the table schema management process within the coordinator. This change is the first step in that direction. In the new arrangement, the coordinator will take on the responsibility of querying both data nodes and tasks to fetch segment schema and subsequently building the table schema. Brokers will now simply query the Coordinator to fetch table schema. Importantly, brokers will still retain the capability to build table schemas if the need arises, ensuring both flexibility and resilience.
* Add system fields to input sources.
Main changes:
1) The SystemField enum defines system fields "__file_uri", "__file_path",
and "__file_bucket". They are associated with each input entity.
2) The SystemFieldInputSource interface can be added to any InputSource
to make it system-field-capable. It sets up serialization of a list
of configured "systemFields" in the JSON form of the input source, and
provides a method getSystemFieldValue for computing the value of each
system field. Cloud object, HDFS, HTTP, and Local now have this.
* Fix various LocalInputSource calls.
* Fix style stuff.
* Fixups.
* Fix tests and coverage.
* Use min of scheduler threads and server threads for subquery guardrails.
This allows more memory to be used for subqueries when the query scheduler
is configured to limit queries below the number of server threads. The patch
also refactors the code so SubqueryGuardrailHelper is provided by a Guice
Provider rather than being created by ClientQuerySegmentWalker, to achieve
better separation of concerns.
* Exclude provider from coverage.
Functions that accept literals also allow casted literals. This shouldn't have an impact on the queries that the user writes. It enables the SQL functions to accept explicit cast, which is required with JDBC.
* Ability to send task types to k8s or worker task runner
* add more tests
* use runnerStrategy to determine task runner
* minor refine
* refine runner strategy config
* move workerType config to upper level
* validate config when application start
Sensor
dependabot[bot]
George Shiqi Wu
Jamie
Suneet Saldanha
Tom
Abhishek Radhakrishnan
Pramod Immaneni
Atul Mohan
Zoltan Haindrich
Hiroshi Fukada
Karan Kumar
zachjsh
Maytas Monsereenusorn
Ben Sykes
YongGang
Rishabh Singh
Clint Wylie
Gian Merlino
Jan Werner
Bartosz Mikulski
Kashif Faraz
Kengo Seki
Laksh Singla