Adds a set of benchmark queries for measuring the planning time with the IN operator. Current results indicate that with the recent optimizations, the IN planning time with 100K expressions in the IN clause is just 3s and with 1M is 46s. For IN clause paired with OR <col>=<val> expr, the numbers are 10s and 155s for 100K and 1M, resp.
The PR makes 2 change:
Correct the current logs directory tarred in GHA static checks to log
Make the steps of logs tar-ing and uploading conditional on web console test failures, which currently happens on any step failure in static checks workflow
Sample logs before this change for failed static checks: https://github.com/apache/druid/actions/runs/7719743853/job/21043502498
* 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>
Fixes a bug introduced in #15609, where queries involving filters on
TIME_FLOOR could encounter ClassCastException when comparing RangeValue
in CombineAndSimplifyBounds.
Prior to #15609, CombineAndSimplifyBounds would remove, rebuild, and
re-add all numeric range filters as part of consolidating numeric range
filters for the same column under the least restrictive type. #15609
included a change to only rebuild numeric range filters when a consolidation
opportunity actually arises. The bug was introduced because the unconditional
rebuild, as a side effect, masked the fact that in some cases range filters
would be created with string match values and a LONG match value type.
This patch changes the fixup to happen at the time the range filter is
initially created, rather than in CombineAndSimplifyBounds.
Nested columns maintain a null value bitmap for which rows are nulls, however I forgot to wire up a ColumnIndexSupplier to nested columns when filtering the 'raw' data itself, so these were not able to be used. This PR fixes that by adding a supplier that can return NullValueIndex to be used by the NullFilter, which should speed up is null and is not null filters on json columns.
I haven't spent the time to measure the difference yet, but I imagine it should be a significant speed increase.
Note that I only wired this up if druid.generic.useDefaultValueForNull=false (sql compatible mode), the reason being that the SQL planner still uses selector filter, which is unable to properly handle any arrays or complex types (including json, even checking for nulls). The reason for this is so that the behavior is consistent between using the index and using the value matcher, otherwise we get into a situation where using the index has correct behavior but using the value matcher does not, which I was trying to avoid.
### Description
The unusedSegment api response was extended to include the original DataSegment object with the creation time and last used update time added to it. A new object `DataSegmentPlus` was created for this purpose, and the metadata queries used were updated as needed.
example response:
```
[
{
"dataSegment": {
"dataSource": "inline_data",
"interval": "2023-01-02T00:00:00.000Z/2023-01-03T00:00:00.000Z",
"version": "2024-01-25T16:06:42.835Z",
"loadSpec": {
"type": "local",
"path": "/Users/zachsherman/projects/opensrc-druid/distribution/target/apache-druid-30.0.0-SNAPSHOT/var/druid/segments/inline_data/2023-01-02T00:00:00.000Z_2023-01-03T00:00:00.000Z/2024-01-25T16:06:42.835Z/0/index/"
},
"dimensions": "str_dim,double_measure1,double_measure2",
"metrics": "",
"shardSpec": {
"type": "numbered",
"partitionNum": 0,
"partitions": 1
},
"binaryVersion": 9,
"size": 1402,
"identifier": "inline_data_2023-01-02T00:00:00.000Z_2023-01-03T00:00:00.000Z_2024-01-25T16:06:42.835Z"
},
"createdDate": "2024-01-25T16:06:44.196Z",
"usedStatusLastUpdatedDate": "2024-01-25T16:07:34.909Z"
}
]
```
A more ideal/permanent fix would be to have status checks exposed by the services,
but that'll require more code changes. So temporarily bump it to unblock CI now.
As part of becoming FIPS compliance, we are seeing this error: salt must be at least 128 bits when we run the Druid code against FIPS Compliant cryptographic security providers.
This PR fixes the salt size used in Pac4jSessionStore.java
* Merge hydrant runners flatly for realtime queries.
Prior to this patch, we have two layers of mergeRunners for realtime
queries: one for each Sink (a logical segment) and one across all
Sinks. This is done because to keep metrics and results grouped by Sink,
given that each FireHydrant within a Sink has its own separate storage
adapter.
However, it costs extra memory usage due to the extra layer of
materialization. This is especially pronounced for groupBy queries,
which only use their merge buffers at the top layer of merging. The
lower layer of merging materializes ResultRows directly into the heap,
which can cause heap exhaustion if there are enough ResultRows.
This patch changes to a single layer of merging when bySegment: false,
just like Historicals. To accommodate that, segment metrics like
query/segment/time are now per-FireHydrant instead of per-Sink.
Two layers of merging are retained when bySegment: true. This isn't
common, because it's typically only used when segment level caching
is enabled on the Broker, which is off by default.
* Use SinkQueryRunners.
* Remove unused method.
* 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
A low value of inSubQueryThreshold can cause queries with IN filter to plan as joins more commonly. However, some of these join queries may not get planned as IN filter on data nodes and causes significant perf regression.
### 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
* Kill tasks should honor the buffer period of unused segments.
- The coordinator duty KillUnusedSegments determines an umbrella interval
for each datasource to determine the kill interval. There can be multiple unused
segments in an umbrella interval with different used_status_last_updated timestamps.
For example, consider an unused segment that is 30 days old and one that is 1 hour old. Currently
the kill task after the 30-day mark would kill both the unused segments and not retain the 1-hour
old one.
- However, when a kill task is instantiated with this umbrella interval, it’d kill
all the unused segments regardless of the last updated timestamp. We need kill
tasks and RetrieveUnusedSegmentsAction to honor the bufferPeriod to avoid killing
unused segments in the kill interval prematurely.
* Clarify default behavior in docs.
* test comments
* fix canDutyRun()
* small updates.
* checkstyle
* forbidden api fix
* doc fix, unused import, codeql scan error, and cleanup logs.
* Address review comments
* Rename maxUsedFlagLastUpdatedTime to maxUsedStatusLastUpdatedTime
This is consistent with the column name `used_status_last_updated`.
* Apply suggestions from code review
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Make period Duration type
* Remove older variants of runKilLTask() in OverlordClient interface
* Test can now run without waiting for canDutyRun().
* Remove previous variants of retrieveUnusedSegments from internal metadata storage coordinator interface.
Removes the following interface methods in favor of a new method added:
- retrieveUnusedSegmentsForInterval(String, Interval)
- retrieveUnusedSegmentsForInterval(String, Interval, Integer)
* Chain stream operations
* cleanup
* Pass in the lastUpdatedTime to markUnused test function and remove sleep.
---------
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Undocument unused segments retrieval API.
* Mark API deprecated and unstable. Note that it'll be removed.
* Cleanup .spelling entries
* Remove the Unstable annotation
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.
* IncrementalIndex#add is no longer thread-safe.
Following #14866, there is no longer a reason for IncrementalIndex#add
to be thread-safe.
It turns out it already was not using its selectors in a thread-safe way,
as exposed by #15615 making `testMultithreadAddFactsUsingExpressionAndJavaScript`
in `IncrementalIndexIngestionTest` flaky. Note that this problem isn't
new: Strings have been stored in the dimension selectors for some time,
but we didn't have a test that checked for that case; we only have
this test that checks for concurrent adds involving numeric selectors.
At any rate, this patch changes OnheapIncrementalIndex to no longer try
to offer a thread-safe "add" method. It also improves performance a bit
by adding a row ID supplier to the selectors it uses to read InputRows,
meaning that it can get the benefit of caching values inside the selectors.
This patch also:
1) Adds synchronization to HyperUniquesAggregator and CardinalityAggregator,
which the similar datasketches versions already have. This is done to
help them adhere to the contract of Aggregator: concurrent calls to
"aggregate" and "get" must be thread-safe.
2) Updates OnHeapIncrementalIndexBenchmark to use JMH and moves it to the
druid-benchmarks module.
* Spelling.
* Changes from static analysis.
* Fix javadoc.
* Clear "lineSplittable" for JSON when using KafkaInputFormat.
JsonInputFormat has a "withLineSplittable" method that can be used to
control whether JSON is read line-by-line, or as a whole. The intent
is that in streaming ingestion, "lineSplittable" is false (although it
can be overridden by "assumeNewlineDelimited"), and in batch ingestion,
lineSplittable is true.
When a "json" format is wrapped by a "kafka" format, this isn't set
properly. This patch updates KafkaInputFormat to set this on an
underlying "json" format.
The tests for KafkaInputFormat were overriding the "lineSplittable"
parameter explicitly, which wasn't really fair, because that made them
unrealistic to what happens in production. Now they omit the parameter
and get the production behavior.
* Add test.
* Fix test coverage.
* Faster parsing: reduce String usage, list-based input rows.
Three changes:
1) Reworked FastLineIterator to optionally avoid generating Strings
entirely, and reduce copying somewhat. Benefits the line-oriented
JSON, CSV, delimited (TSV), and regex formats.
2) In the delimited (TSV) format, when the delimiter is a single byte,
split on UTF-8 bytes directly.
3) In CSV and delimited (TSV) formats, use list-based input rows when
the column list is provided upfront by the user.
* Fix style.
* Fix inspections.
* Restore validation.
* Remove fastutil-extra.
* Exception type.
* Fixes for error messages.
* Fixes for null handling.
This PR fixes the summary iterator to add aggregators in the correct position. The summary iterator is used when dims are not present, therefore the new change is identical to the old one, but seems more correct while reading.
Vishesh Garg
Zoltan Haindrich
Abhishek Radhakrishnan
Benjamin Hopp
AmatyaAvadhanula
George Shiqi Wu
317brian
Vadim Ogievetsky
Gian Merlino
Clint Wylie
Abhishek Agarwal
zachjsh
Sensor
Katya Macedo
Parth Agrawal
Hiroshi Fukada
Karan Kumar
Pranav
Benedict Jin
Tom
Maytas Monsereenusorn
Laksh Singla