MSQ now allows empty ingest queries by default. For such queries that don't generate any output rows, the query counters in the async status result object/task report don't contain numTotalRows and totalSizeInBytes. These properties when not set/undefined can be confusing to API clients. For example, the web-console treats it as unknown values.
This patch fixes the counters by explicitly reporting them as 0 instead of null for empty ingest queries.
* support groups windowing mode; which is a close relative of ranges (but not in the standard)
* all windows with range expressions will be executed wit it groups
* it will be 100% correct in case for both bounds its true that: isCurrentRow() || isUnBounded()
* this covers OVER ( ORDER BY COL )
* for other cases it will have some chances of getting correct results...
Changes:
- Add new task context flag useConcurrentLocks.
- This can be set for an individual task or at a cluster level using `druid.indexer.task.default.context`.
- When set to true, any appending task would use an APPEND lock and any other
ingestion task would use a REPLACE lock when using time chunk locking.
- If false (default), we fall back on the context flag taskLockType and then useSharedLock.
* Add ImmutableLookupMap for static lookups.
This patch adds a new ImmutableLookupMap, which comes with an
ImmutableLookupExtractor. It uses a fastutil open hashmap plus two
lists to store its data in such a way that forward and reverse
lookups can both be done quickly. I also observed footprint to be
somewhat smaller than Java HashMap + MapLookupExtractor for a 1 million
row lookup.
The main advantage, though, is that reverse lookups can be done much
more quickly than MapLookupExtractor (which iterates the entire map
for each call to unapplyAll). This speeds up the recently added
ReverseLookupRule (#15626) during SQL planning with very large lookups.
* Use in one more test.
* Fix benchmark.
* Object2ObjectOpenHashMap
* Fixes, and LookupExtractor interface update to have asMap.
* Remove commented-out code.
* Fix style.
* Fix import order.
* Add fastutil.
* Avoid storing Map entries.
* Faster k-way merging using tournament trees, 8-byte key strides.
Two speedups for FrameChannelMerger (which does k-way merging in MSQ):
1) Replace the priority queue with a tournament tree, which does fewer
comparisons.
2) Compare keys using 8-byte strides, rather than 1 byte at a time.
* Adjust comments.
* Fix style.
* Adjust benchmark and test.
* Add eight-list test (power of two).
Add class PasswordHashGenerator. Move hashing logic from BasicAuthUtils to this new class.
Add cache in the hash generator to contain the computed hash of passwords and boost validator performance
Cache has max size 1000 and expiry 1 hour
Key of the cache is an SHA-256 hash of the (password + random salt generated on service startup)
Currently, If 2 tasks are consuming from the same partitions, try to publish the segment and update the metadata, the second task can fail because the end offset stored in the metadata store doesn't match with the start offset of the second task. We can fix this by retrying instead of failing.
AFAIK apart from the above issue, the metadata mismatch can happen in 2 scenarios:
- when we update the input topic name for the data source
- when we run 2 replicas of ingestion tasks(1 replica will publish and 1 will fail as the first replica has already updated the metadata).
Implemented the comparable function to compare the last committed end offset and new Sequence start offset. And return a specific error msg for this.
Add retry logic on indexers to retry for this specific error msg.
Updated the existing test case.
Added support for Azure Government storage in Druid Azure-Extensions. This enhancement allows the Azure-Extensions to be compatible with different Azure storage types by updating the endpoint suffix from a hardcoded value to a configurable one.
* 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
FILTER_INTO_JOIN is mainly run along with the other rules with the Volcano planner; however if the query starts highly underdefined (join conditions in the where clauses) that generic query could give a lot of room for the other rules to play around with only enabled it for when the join uses subqueries for its inputs.
PROJECT_FILTER rule is not that useful. and could increase planning times by providing new plans. This problem worsened after we started supporting inner joins with arbitrary join conditions in https://github.com/apache/druid/pull/15302
* 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.
* Allow empty inserts and replace.
- Introduce a new query context failOnEmptyInsert which defaults to false.
- When this context is false (default), MSQE will now allow empty inserts and replaces.
- When this context is true, MSQE will throw the existing InsertCannotBeEmpty MSQ fault.
- For REPLACE ALL over an ALL grain segment, the query will generate a tombstone spanning eternity
which will be removed eventually be the coordinator.
- Add unit tests in MSQInsertTest, MSQReplaceTest to test the new default behavior (i.e., when failOnEmptyInsert = false)
- Update unit tests in MSQFaultsTest to test the non-default behavior (i.e., when failOnEmptyInsert = true)
* Ignore test to see if it's the culprit for OOM
* Add heap dump config
* Bump up -Xmx from 1500 MB to 2048 MB
* Add steps to tarball and collect hprof dump to GHA action
* put back mx to 1500MB to trigger the failure
* add the step to reusable unit test workflow as well
* Revert the temp heap dump & @Ignore changes since max heap size is increased
* Minor updates
* Review comments
1. Doc suggestions
2. Add tests for empty insert and replace queries with ALL grain and limit in the
default failOnEmptyInsert mode (=false). Add similar tests to MSQFaultsTest with
failOnEmptyInsert = true, so the query does fail with an InsertCannotBeEmpty fault.
3. Nullable annotation and javadocs
* Add comment
replace_limit.patch
Changes
- Add `log` implementation for `AuditManager` alongwith `SQLAuditManager`
- `LoggingAuditManager` simply logs the audit event. Thus, it returns empty for
all `fetchAuditHistory` calls.
- Add new config `druid.audit.manager.type` which can take values `log`, `sql` (default)
- Add new config `druid.audit.manager.logLevel` which can take values `DEBUG`, `INFO`, `WARN`.
This gets activated only if `type` is `log`.
- Remove usage of `ConfigSerde` from `AuditManager` as audit is not just limited to configs
- Add `AuditSerdeHelper` for a single implementation of serialization/deserialization of
audit payload and other utility methods.
The PR addresses 2 things:
Add MSQ durable storage connector for GCS
Change GCS client library from the old Google API Client Library to the recommended Google Cloud Client Library. Ref: https://cloud.google.com/apis/docs/client-libraries-explained
* Upgrade org.pac4j:pac4j-oidc to 4.5.5 to address CVE-2021-44878
* add CVE suppression and notes, since vulnerability scan still shows this CVE
* Add tests to improve coverage
Update of direct dependencies:
* kubernetes java-client to 19.0.0
* docker-java-bom to 3.3.4
In order to update transitive dependencies:
* okio to 3.6.0
* bcjava to 1.76
To address CVES:
- CVE-2023-3635 in okio
- CVE-2023-33201 in bcjava
---------
Co-authored-by: Xavier Léauté <xvrl@apache.org>
Fixes a potential NPE which could occur while folding the HllSketchAggregator. If the sketch is null, druid could return a null HllSketchHolder object. Adding a null check here could help here
Resolves a null pointer exception in HllSketchAggregatorFactory
This change completes the change introduced in #15461
and unifies the version of gson dependency used between all the modules.
gson is used by kubernetes-extension, avro-extensions, ranger-security,
and as a test dependency in several core modules.
---------
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
* Excluding jackson-jaxrs dependency from ranger-plugin-common to address CVE regression introduced by ranger-upgrade: CVE-2019-10202, CVE-2019-10172
* remove the reference to outdated ranger 2.0 from the docs
---------
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
Recent upgrade of ranger introduced CVE regressions due to outdated elasticsearch components.
Druid-ranger-plugin does not elasticsearch components , and they have been explicitly removed.
Update woodstox-core to 6.4.0 to address GHSA-3f7h-mf4q-vrm4
This PR revives #14978 with a few more bells and whistles. Instead of an unconditional cross-join, we will now split the join condition such that some conditions are now evaluated post-join. To decide what sub-condition goes where, I have refactored DruidJoinRule class to extract unsupported sub-conditions. We build a postJoinFilter out of these unsupported sub-conditions and push to the join.
* update confluent's dependencies to common, supported version
Update io.confluent.* dependencies to common, updated version 6.2.12
currently used versions are EOL
* move version definition to the top level pom
Changes:
- Fix log `Got end of partition marker for partition [%s] from task [%s] in discoverTasks`
by fixing order of args
- Simplify in-line classes by using lambda
- Update kill task message from `Task [%s] failed to respond to [set end offsets]
in a timely manner, killing task` to `Failed to set end offsets, killing task`
- Clean up tests
There is a problem with Quantiles sketches and KLL Quantiles sketches.
Queries using the histogram post-aggregator fail if:
- the sketch contains at least one value, and
- the values in the sketch are all equal, and
- the splitPoints argument is not passed to the post-aggregator, and
- the numBins argument is greater than 2 (or not specified, which
leads to the default of 10 being used)
In that case, the query fails and returns this error:
{
"error": "Unknown exception",
"errorClass": "org.apache.datasketches.common.SketchesArgumentException",
"host": null,
"errorCode": "legacyQueryException",
"persona": "OPERATOR",
"category": "RUNTIME_FAILURE",
"errorMessage": "Values must be unique, monotonically increasing and not NaN.",
"context": {
"host": null,
"errorClass": "org.apache.datasketches.common.SketchesArgumentException",
"legacyErrorCode": "Unknown exception"
}
}
This behaviour is undesirable, since the caller doesn't necessarily
know in advance whether the sketch has values that are diverse
enough. With this change, the post-aggregators return [N, 0, 0...]
instead of crashing, where N is the number of values in the sketch,
and the length of the list is equal to numBins. That is what they
already returned for numBins = 2.
Here is an example of a query that would fail:
{"queryType":"timeseries",
"dataSource": {
"type": "inline",
"columnNames": ["foo", "bar"],
"rows": [
["abc", 42.0],
["def", 42.0]
]
},
"intervals":["0000/3000"],
"granularity":"all",
"aggregations":[
{"name":"the_sketch", "fieldName":"bar", "type":"quantilesDoublesSketch"}],
"postAggregations":[
{"name":"the_histogram",
"type":"quantilesDoublesSketchToHistogram",
"field":{"type":"fieldAccess","fieldName":"the_sketch"},
"numBins": 3}]}
I believe this also fixes issue #10585.
* Fix capacity response in mm-less ingestion (#14888)
Changes:
- Fix capacity response in mm-less ingestion.
- Add field usedClusterCapacity to the GET /totalWorkerCapacity response.
This API should be used to get the total ingestion capacity on the overlord.
- Remove method `isK8sTaskRunner` from interface `TaskRunner`
* Using Map to perform comparison
* Minor Change
---------
Co-authored-by: George Shiqi Wu <george.wu@imply.io>
Saw bug where MSQ controller task would continue to hold the task slot even after cancel was issued.
This was due to a deadlock created on work launch. The main thread was waiting for tasks to spawn and the cancel thread was waiting for tasks to finish.
The fix was to instruct the MSQWorkerTaskLauncher thread to stop creating new tasks which would enable the main thread to unblock and release the slot.
Also short circuited the taskRetriable condition. Now the check is run in the MSQWorkerTaskLauncher thread as opposed to the main event thread loop. This will result in faster task failure in case the task is deemed to be non retriable.
* MSQ generates tombstones honoring the query's granularity.
This change tweaks to only account for the infinite-interval tombstones.
For finite-interval tombstones, the MSQ query granualrity will be used
which is consistent with how MSQ works.
* more tests and some cleanup.
* checkstyle
* comment edits
* Throw TooManyBuckets fault based on review; add more tests.
* Add javadocs for both methods on reconciling the methods.
* review: Move testReplaceTombstonesWithTooManyBucketsThrowsException to MsqFaultsTest
* remove unused imports.
* Move TooManyBucketsException to indexing package for shared exception handling.
* lower max bucket for tests and fixup count
* Advance and count the iterator.
* checkstyle
* + Fix for Flaky Test
* + Replacing TreeMap with LinkedHashMap
* + Changing data structure from LinkedHashMap to HashMap
* Fixed flaky test in S3DataSegmentPusherConfigTest.testSerializationValidatingMaxListingLength
* Minor Changes
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.
* Use filters for pruning properly for hash-joins.
Native used them too aggressively: it might use filters for the RHS
to prune the LHS. MSQ used them not at all. Now, both use them properly,
pruning based on base (LHS) columns only.
* Fix tests.
* Fix style.
* Clear filterFields too.
* Update.
* 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.
* better documentation for the differences between arrays and mvds
* add outputType to ExpressionPostAggregator to make docs true
* add output coercion if outputType is defined on ExpressionPostAgg
* updated post-aggregations.md to be consistent with aggregations.md and filters.md and use tables
While running queries on real time tasks using MSQ, there is an issue with queries with certain order by columns.
If the query specifies a non time column, the query is planned as it is supported by MSQ. However, this throws an exception when passed to real time tasks once as the native query stack does not support it. This PR resolves this by removing the ordering from the query before contacting real time tasks.
Fixes a bug with MSQ while reading data from real time tasks with non time ordering
