* 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.
* New handling for COALESCE, SEARCH, and filter optimization.
COALESCE is converted by Calcite's parser to CASE, which is largely
counterproductive for us, because it ends up duplicating expressions.
In the current code we end up un-doing it in our CaseOperatorConversion.
This patch has a different approach:
1) Add CaseToCoalesceRule to convert CASE back to COALESCE earlier, before
the Volcano planner runs, using CaseToCoalesceRule.
2) Add FilterDecomposeCoalesceRule to decompose calls like
"f(COALESCE(x, y))" into "(x IS NOT NULL AND f(x)) OR (x IS NULL AND f(y))".
This helps use indexes when available on x and y.
3) Add CoalesceLookupRule to push COALESCE into the third arg of LOOKUP.
4) Add a native "coalesce" function so we can convert 3+ arg COALESCE.
The advantage of this approach is that by un-doing the CASE to COALESCE
conversion earlier, we have flexibility to do more stuff with
COALESCE (like decomposition and pushing into LOOKUP).
SEARCH is an operator used internally by Calcite to represent matching
an argument against some set of ranges. This patch improves our handling
of SEARCH in two ways:
1) Expand NOT points (point "holes" in the range set) from SEARCH as
`!(a || b)` rather than `!a && !b`, which makes it possible to convert
them to a "not" of "in" filter later.
2) Generate those nice conversions for NOT points even if the SEARCH
is not composed of 100% NOT points. Without this change, a SEARCH
for "x NOT IN ('a', 'b') AND x < 'm'" would get converted like
"x < 'a' OR (x > 'a' AND x < 'b') OR (x > 'b' AND x < 'm')".
One of the steps we take when generating Druid queries from Calcite
plans is to optimize native filters. This patch improves this step:
1) Extract common ANDed predicates in ConvertSelectorsToIns, so we can
convert "(a && x = 'b') || (a && x = 'c')" into "a && x IN ('b', 'c')".
2) Speed up CombineAndSimplifyBounds and ConvertSelectorsToIns on
ORs with lots of children by adjusting the logic to avoid calling
"indexOf" and "remove" on an ArrayList.
3) Refactor ConvertSelectorsToIns to reduce duplicated code between the
handling for "selector" and "equals" filters.
* Not so final.
* Fixes.
* Fix test.
* Fix test.
* Fix ColumnSelectorColumnIndexSelector#getColumnCapabilities.
It was using virtualColumns.getColumnCapabilities, which only returns
capabilities for virtual columns, not regular columns. The effect of this
is that expression filters (and in some cases, arrayContainsElement filters)
would build value matchers rather than use indexes.
I think this has been like this since #12315, which added the
getColumnCapabilities method to BitmapIndexSelector, and included the same
implementation as exists in the code today.
This error is easy to make due to the design of virtualColumns.getColumnCapabilities,
so to help avoid it in the future, this patch renames the method to
getColumnCapabilitiesWithoutFallback to emphasize that it does not return
capabilities for regular columns.
* Make getColumnCapabilitiesWithoutFallback package-private.
* Fix expression filter bitmap usage.
The PR: #13947 introduced a function evalDimension() in the interface RowFunction.
There was no default implementation added for this interface which causes all the implementations and custom transforms to fail and require to implement their own version of evalDimension method. This PR adds a default implementation in the interface which allows the evalDimension to return value as a Singleton array of eval result.
Fixes#15072
Before this modification , the third parameter (timezone) require to be a Literal, it will throw a error when this parameter is column Identifier.
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.
* 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
I was looking into a query which was performing a bit poorly because the case_searched was touching more than 1 columns (if there is only 1 column there is a cache based evaluator).
While I was doing that I've noticed that there are a few simple things which could help a bit:
use a static TRUE/FALSE instead of creating a new object every time
create the ExprEval early for ConstantExpr -s (except the one for BigInteger which seem to have some odd contract)
return early from type autodetection
these changes mostly reduce the amount of garbage the query creates during case_searched evaluation; although ExpressionSelectorBenchmark shows some improvements ~15% - but my manual trials on the taxi dataset with 60M rows showed more improvements - probably due to the fact that these changes mostly only reduce gc pressure.
* Add initial draft of MarkDanglingTombstonesAsUnused duty.
* Use overshadowed segments instead of all used segments.
* Add unit test for MarkDanglingSegmentsAsUnused duty.
* Add mock call
* Simplify code.
* Docs
* shorter lines formatting
* metric doc
* More tests, refactor and fix up some logic.
* update javadocs; other review comments.
* Make numCorePartitions as 0 in the TombstoneShardSpec.
* fix up test
* Add tombstone core partition tests
* Update docs/design/coordinator.md
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
* review comment
* Minor cleanup
* Only consider tombstones with 0 core partitions
* Need to register the test shard type to make jackson happy
* test comments
* checkstyle
* fixup misc typos in comments
* Update logic to use overshadowed segments
* minor cleanup
* Rename duty to eternity tombstone instead of dangling. Add test for full eternity tombstone.
* Address review feedback.
---------
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
Query with lookups in FilteredAggregator fails with this exception in router,
Cannot construct instance of `org.apache.druid.query.aggregation.FilteredAggregatorFactory`, problem: Lookup [campaigns_lookup[campaignId][is_sold][autodsp]] not found at [Source: (org.eclipse.jetty.server.HttpInputOverHTTP); line: 1, column: 913] (through reference chain: org.apache.druid.query.groupby.GroupByQuery["aggregations"]->java.util.ArrayList[1])
T
he problem is that constructor of FilteredAggregatorFactory is actually validating if the lookup exists in this statement dimFilter.toFilter().
This is failing on the router, which is to be expected, because, the router isn’t assigned any lookups.
The fix is to move to a lazy initialisation of the filter object in the constructor.
It wasn't checking the column name, so it would return a domain regardless
of the input column. This means that null filters on data sources with range
partitioning would lead to excessive pruning of segments, and therefore
missing results.
I think this is a problem as it discards the false return value when the putToKeyBuffer can't store the value because of the limit
Not forwarding the return value at that point may lead to the normal continuation here regardless something was not added to the dictionary like here
* Make numCorePartitions as 0 in the TombstoneShardSpec.
* fix up test
* Add tombstone core partition tests
* review comment
* Need to register the test shard type to make jackson happy
Fixed the following flaky tests:
org.apache.druid.math.expr.ParserTest#testApplyFunctions
org.apache.druid.math.expr.ParserTest#testSimpleMultiplicativeOp1
org.apache.druid.math.expr.ParserTest#testFunctions
org.apache.druid.math.expr.ParserTest#testSimpleLogicalOps1
org.apache.druid.math.expr.ParserTest#testSimpleAdditivityOp1
org.apache.druid.math.expr.ParserTest#testSimpleAdditivityOp2
The above mentioned tests have been reported as flaky (tests assuming deterministic implementation of a non-deterministic specification ) when ran against the NonDex tool.
The tests contain assertions (Assertion 1 & Assertion 2) that compare an ArrayList created from a HashSet using the ArrayList() constructor with another List. However, HashSet does not guarantee the ordering of elements and thus resulting in these flaky tests that assume deterministic implementation of HashSet. Thus, when the NonDex tool shuffles the HashSet elements, it results in the test failures:
Co-authored-by: ythorat2 <ythorat2@illinois.edu>
* 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
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
* Frames: consider writing singly-valued column when input column hasMultipleValues is UNKNOWN.
Prior to this patch, columnar frames would always write multi-valued columns if
the input column had hasMultipleValues = UNKNOWN. This had the effect of flipping
UNKNOWN to TRUE when copying data into frames, which is problematic because TRUE
causes expressions to assume that string inputs must be treated as arrays.
We now avoid this by flipping UNKNOWN to FALSE if no multi-valuedness
is encountered, and flipping it to TRUE if multi-valuedness is encountered.
* Add regression test case.
Currently advance function in postJoinCursor calls advanceUninterruptibly which in turn keeps calling baseCursor.advanceUninterruptibly until the post join condition matches, without checking for interrupts. This causes the CPU to hit 100% without getting a chance for query to be cancelled.
With this change, the call flow of advance and advanceUninterruptibly is separated out so that they call baseCursor.advance and baseCursor.advanceUninterruptibly in them, respectively, giving a chance for interrupts in the former case between successive calls to baseCursor.advance.
* Fix error assuming a Complex Type that is a Number is a double
In the case where a complex type is a number, it may not be castable to double. It can safely be case as Number first to get to the doubleValue.
- adds a new query build path: DruidQuery#toScanAndSortQuery which:
- builds a ScanQuery without considering the current ordering
- builds an operator to execute the sort
- fixes a null string to "null" literal string conversion in the frame serializer code
- fixes some DrillWindowQueryTest cases
- fix NPE in NaiveSortOperator in case there was no input
- enables back CoreRules.AGGREGATE_REMOVE
- adds a processing level OffsetLimit class and uses that instead of just the limit in the rac parts
- earlier window expressions on top of a subquery with an offset may have ignored the offset
Clint Wylie
Gian Merlino
Parth Agrawal
AlbericByte
Kashif Faraz
Tom
Zoltan Haindrich
Soumyava
zachjsh
Ankit Kothari
Abhishek Radhakrishnan
Rishabh Singh
sb89594
Pranav
Yashdeep Thorat
Adarsh Sanjeev
Vishesh Garg
Ben Sykes
Zoltan Haindrich
Simon Hofbauer