* MSQ: Use the same result coercion routines as the regular SQL endpoint.
The main changes are to move NativeQueryMaker.coerce to SqlResults, and
to formally make the list of sqlTypeNames from the MSQ results reports
use SqlTypeNames.
- Change the default to MSQ-compatible rather than MSQ-incompatible.
The explicit marker function is now "notMsqCompatible()".
* MSQ: Support for querying lookup and inline data directly.
Main changes:
1) Add of LookupInputSpec and DataSourcePlan.forLookup.
2) Add InlineInputSpec, and modify of DataSourcePlan.forInline to use
this instead of an ExternalInputSpec with JSON. This allows the inline
data to act as the right-hand side of a join, if needed.
Supporting changes:
1) Modify JoinDataSource's leftFilter validation to be a little less
strict: it's now OK with leftFilter being attached to any concrete
leaf (no children) datasource, rather than requiring it be a table.
This allows MSQ to create JoinDataSource with InputNumberDataSource
as the base.
2) Add SegmentWranglerModule to CliIndexer, CliPeon. This allows them to
query lookups and inline data directly.
* Updates based on CI.
* Additional tests.
* Style fix.
* Remove unused import.
The GCP initialization pulls credentials for
talking to GCP. We want that to only happen
when fully required and thus want the GCP-related
objects lazily instantiated.
* MSQ: Support multiple result columns with the same name.
This is allowed in SQL, and is supported by the regular SQL endpoint.
We retain a validation that INSERT ... SELECT does not allow multiple
columns with the same name, because column names in segments must be
unique.
### Description
Previously msq controller and worker tasks did not have implementations for the `getInputSourceResources()` method. This causes the submission of these tasks to fail if the following auth config is enabled:
`druid.auth.enableInputSourceSecurity=true`
Added implementations of this method for these tasks that return an empty set of input sources. This means that for these task types, if `druid.auth.enableInputSourceSecurity=true` config is used, the input source types will be properly computed and authorized in the SQL layer, but not if the equivalent controller / worker tasks are submitted to the task endpoint.
### Description
This change allows for input sources used during MSQ ingestion to be authorized for multiple input source types, instead of just 1. Such an input source that allows for multiple types is the CombiningInputSource.
Also fixed bug that caused some input source specific functions to be authorized against the permissions
`
[
new ResourceAction(new Resource(ResourceType.EXTERNAL, ResourceType.EXTERNAL), Action.READ),
new ResourceAction(new Resource(ResourceType.EXTERNAL, {input_source_type}), Action.READ)
]
`
when the inputSource based authorization feature is enabled, when it should instead be authorized against
`
[
new ResourceAction(new Resource(ResourceType.EXTERNAL, {input_source_type}), Action.READ)
]
`
* Frames: Ensure nulls are read as default values when appropriate.
Fixes a bug where LongFieldWriter didn't write a properly transformed
zero when writing out a null. This had no meaningful effect in SQL-compatible
null handling mode, because the field would get treated as a null anyway.
But it does have an effect in default-value mode: it would cause Long.MIN_VALUE
to get read out instead of zero.
Also adds NullHandling checks to the various frame-based column selectors,
allowing reading of nullable frames by servers in default-value mode.
Fixes#13837.
### Description
This change allows for input source type security in the native task layer.
To enable this feature, the user must set the following property to true:
`druid.auth.enableInputSourceSecurity=true`
The default value for this property is false, which will continue the existing functionality of needing authorization to write to the respective datasource.
When this config is enabled, the users will be required to be authorized for the following resource action, in addition to write permission on the respective datasource.
`new ResourceAction(new Resource(ResourceType.EXTERNAL, {INPUT_SOURCE_TYPE}, Action.READ`
where `{INPUT_SOURCE_TYPE}` is the type of the input source being used;, http, inline, s3, etc..
Only tasks that provide a non-default implementation of the `getInputSourceResources` method can be submitted when config `druid.auth.enableInputSourceSecurity=true` is set. Otherwise, a 400 error will be thrown.
* Always use file sizes when determining batch ingest splits.
Main changes:
1) Update CloudObjectInputSource and its subclasses (S3, GCS,
Azure, Aliyun OSS) to use SplitHintSpecs in all cases. Previously, they
were only used for prefixes, not uris or objects.
2) Update ExternalInputSpecSlicer (MSQ) to consider file size. Previously,
file size was ignored; all files were treated as equal weight when
determining splits.
A side effect of these changes is that we'll make additional network
calls to find the sizes of objects when users specify URIs or objects
as opposed to prefixes. IMO, this is worth it because it's the only way
to respect the user's split hint and task assignment settings.
Secondary changes:
1) S3, Aliyun OSS: Use getObjectMetadata instead of listObjects to get
metadata for a single object. This is a simpler call that is also
expected to be less expensive.
2) Azure: Fix a bug where getBlobLength did not populate blob
reference attributes, and therefore would not actually retrieve the
blob length.
3) MSQ: Align dynamic slicing logic between ExternalInputSpecSlicer and
TableInputSpecSlicer.
4) MSQ: Adjust WorkerInputs to ensure there is always at least one
worker, even if it has a nil slice.
* Add msqCompatible to testGroupByWithImpossibleTimeFilter.
* Fix tests.
* Add additional tests.
* Remove unused stuff.
* Remove more unused stuff.
* Adjust thresholds.
* Remove irrelevant test.
* Fix comments.
* Fix bug.
* Updates.
* Add a new fault "QueryRuntimeError" to MSQ engine to capture native query errors.
* Fixed bug in MSQ fault tolerance where worker were being retried if `UnexpectedMultiValueDimensionException` was thrown.
* An exception from the query runtime with `org.apache.druid.query` as the package name is thrown as a QueryRuntimeError
changes:
* introduce ColumnFormat to separate physical storage format from logical type. ColumnFormat is now used instead of ColumnCapabilities to get column handlers for segment creation
* introduce new 'auto' type indexer and merger which produces a new common nested format of columns, which is the next logical iteration of the nested column stuff. Essentially this is an automatic type column indexer that produces the most appropriate column for the given inputs, making either STRING, ARRAY<STRING>, LONG, ARRAY<LONG>, DOUBLE, ARRAY<DOUBLE>, or COMPLEX<json>.
* revert NestedDataColumnIndexer, NestedDataColumnMerger, NestedDataColumnSerializer to their version pre #13803 behavior (v4) for backwards compatibility
* fix a bug in RoaringBitmapSerdeFactory if anything actually ever wrote out an empty bitmap using toBytes and then later tried to read it (the nerve!)
This change introduces the concept of input source type security model, proposed in #13837.. With this change, this feature is only available at the SQL layer, but we will expand to native layer in a follow up PR.
To enable this feature, the user must set the following property to true:
druid.auth.enableInputSourceSecurity=true
The default value for this property is false, which will continue the existing functionality of having the usage all external sources being authorized against the hardcoded resource action
new ResourceAction(new Resource(ResourceType.EXTERNAL, ResourceType.EXTERNAL), Action.READ
When this config is enabled, the users will be required to be authorized for the following resource action
new ResourceAction(new Resource(ResourceType.EXTERNAL, {INPUT_SOURCE_TYPE}, Action.READ
where {INPUT_SOURCE_TYPE} is the type of the input source being used;, http, inline, s3, etc..
Documentation has not been added for the feature as it is not complete at the moment, as we still need to enable this for the native layer in a follow up pr.
This PR is a follow-up to #13819 so that the Tuple sketch functionality can be used in SQL for both ingestion using Multi-Stage Queries (MSQ) and also for analytic queries against Tuple sketch columns.
* Reworking s3 connector with
1. Adding retries
2. Adding max fetch size
3. Using s3Utils for most of the api's
4. Fixing bugs in DurableStorageCleaner
5. Moving to Iterator for listDir call
array columns!
changes:
* add support for storing nested arrays of string, long, and double values as specialized nested columns instead of breaking them into separate element columns
* nested column type mimic behavior means that columns ingested with only root arrays of primitive values will be ARRAY typed columns
* neat test refactor stuff
* add v4 segment test
* add array element indexes
* add tests for unnest and array columns
* fix unnest column value selector cursor handling of null and empty arrays
Expands the OIDC based auth in Druid by adding a JWT Authenticator that validates ID Tokens associated with a request. The existing pac4j authenticator works for authenticating web users while accessing the console, whereas this authenticator is for validating Druid API requests made by Direct clients. Services already supporting OIDC can attach their ID tokens to the Druid requests
under the Authorization request header.
* Add segment generator counters to reports
* Remove unneeded annotation
* Fix checkstyle and coverage
* Add persist and merged as new metrics
* Address review comments
* Fix checkstyle
* Create metrics class to handle updating counters
* Address review comments
* Add rowsPushed as a new metrics
changes:
* fixes inconsistent handling of byte[] values between ExprEval.bestEffortOf and ExprEval.ofType, which could cause byte[] values to end up as java toString values instead of base64 encoded strings in ingest time transforms
* improved ExpressionTransform binding to re-use ExprEval.bestEffortOf when evaluating a binding instead of throwing it away
* improved ExpressionTransform array handling, added RowFunction.evalDimension that returns List<String> to back Row.getDimension and remove the automatic coercing of array types that would typically happen to expression transforms unless using Row.getDimension
* added some tests for ExpressionTransform with array inputs
* improved ExpressionPostAggregator to use partial type information from decoration
* migrate some test uses of InputBindings.forMap to use other methods
* Lower default maxRowsInMemory for realtime ingestion.
The thinking here is that for best ingestion throughput, we want
intermediate persists to be as big as possible without using up all
available memory. So, we rely mainly on maxBytesInMemory. The default
maxRowsInMemory (1 million) is really just a safety: in case we have
a large number of very small rows, we don't want to get overwhelmed
by per-row overheads.
However, maximum ingestion throughput isn't necessarily the primary
goal for realtime ingestion. Query performance is also important. And
because query performance is not as good on the in-memory dataset, it's
helpful to keep it from growing too large. 150k seems like a reasonable
balance here. It means that for a typical 5 million row segment, we
won't trigger more than 33 persists due to this limit, which is a
reasonable number of persists.
* Update tests.
* Update server/src/main/java/org/apache/druid/segment/indexing/RealtimeTuningConfig.java
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Fix test.
* Fix link.
---------
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Removing intermediateSuperSorterStorageMaxLocalBytes, maxInputBytesPerWorker, composedIntermediateSuperSorterStorageEnabled, clusterStatisticsMergeMode from docs
* Adding documentation in the context class.
* Various changes and fixes to UNNEST.
Native changes:
1) UnnestDataSource: Replace "column" and "outputName" with "virtualColumn".
This enables pushing expressions into the datasource. This in turn
allows us to do the next thing...
2) UnnestStorageAdapter: Logically apply query-level filters and virtual
columns after the unnest operation. (Physically, filters are pulled up,
when possible.) This is beneficial because it allows filters and
virtual columns to reference the unnested column, and because it is
consistent with how the join datasource works.
3) Various documentation updates, including declaring "unnest" as an
experimental feature for now.
SQL changes:
1) Rename DruidUnnestRel (& Rule) to DruidUnnestRel (& Rule). The rel
is simplified: it only handles the UNNEST part of a correlated join.
Constant UNNESTs are handled with regular inline rels.
2) Rework DruidCorrelateUnnestRule to focus on pulling Projects from
the left side up above the Correlate. New test testUnnestTwice verifies
that this works even when two UNNESTs are stacked on the same table.
3) Include ProjectCorrelateTransposeRule from Calcite to encourage
pushing mappings down below the left-hand side of the Correlate.
4) Add a new CorrelateFilterLTransposeRule and CorrelateFilterRTransposeRule
to handle pulling Filters up above the Correlate. New tests
testUnnestWithFiltersOutside and testUnnestTwiceWithFilters verify
this behavior.
5) Require a context feature flag for SQL UNNEST, since it's undocumented.
As part of this, also cleaned up how we handle feature flags in SQL.
They're now hooked into EngineFeatures, which is useful because not
all engines support all features.
* Sort-merge join and hash shuffles for MSQ.
The main changes are in the processing, multi-stage-query, and sql modules.
processing module:
1) Rename SortColumn to KeyColumn, replace boolean descending with KeyOrder.
This makes it nicer to model hash keys, which use KeyOrder.NONE.
2) Add nullability checkers to the FieldReader interface, and an
"isPartiallyNullKey" method to FrameComparisonWidget. The join
processor uses this to detect null keys.
3) Add WritableFrameChannel.isClosed and OutputChannel.isReadableChannelReady
so callers can tell which OutputChannels are ready for reading and which
aren't.
4) Specialize FrameProcessors.makeCursor to return FrameCursor, a random-access
implementation. The join processor uses this to rewind when it needs to
replay a set of rows with a particular key.
5) Add MemoryAllocatorFactory, which is embedded inside FrameWriterFactory
instead of a particular MemoryAllocator. This allows FrameWriterFactory
to be shared in more scenarios.
multi-stage-query module:
1) ShuffleSpec: Add hash-based shuffles. New enum ShuffleKind helps callers
figure out what kind of shuffle is happening. The change from SortColumn
to KeyColumn allows ClusterBy to be used for both hash-based and sort-based
shuffling.
2) WorkerImpl: Add ability to handle hash-based shuffles. Refactor the logic
to be more readable by moving the work-order-running code to the inner
class RunWorkOrder, and the shuffle-pipeline-building code to the inner
class ShufflePipelineBuilder.
3) Add SortMergeJoinFrameProcessor and factory.
4) WorkerMemoryParameters: Adjust logic to reserve space for output frames
for hash partitioning. (We need one frame per partition.)
sql module:
1) Add sqlJoinAlgorithm context parameter; can be "broadcast" or
"sortMerge". With native, it must always be "broadcast", or it's a
validation error. MSQ supports both. Default is "broadcast" in
both engines.
2) Validate that MSQs do not use broadcast join with RIGHT or FULL join,
as results are not correct for broadcast join with those types. Allow
this in native for two reasons: legacy (the docs caution against it,
but it's always been allowed), and the fact that it actually *does*
generate correct results in native when the join is processed on the
Broker. It is much less likely that MSQ will plan in such a way that
generates correct results.
3) Remove subquery penalty in DruidJoinQueryRel when using sort-merge
join, because subqueries are always required, so there's no reason
to penalize them.
4) Move previously-disabled join reordering and manipulation rules to
FANCY_JOIN_RULES, and enable them when using sort-merge join. Helps
get to better plans where projections and filters are pushed down.
* Work around compiler problem.
* Updates from static analysis.
* Fix @param tag.
* Fix declared exception.
* Fix spelling.
* Minor adjustments.
* wip
* Merge fixups
* fixes
* Fix CalciteSelectQueryMSQTest
* Empty keys are sortable.
* Address comments from code review. Rename mux -> mix.
* Restore inspection config.
* Restore original doc.
* Reorder imports.
* Adjustments
* Fix.
* Fix imports.
* Adjustments from review.
* Update header.
* Adjust docs.
You can now do the following operations with TupleSketches in Post Aggregation Step
Get the Sketch Output as Base64 String
Provide a constant Tuple Sketch in post-aggregation step that can be used in Set Operations
Get the Estimated Value(Sum) of Summary/Metrics Objects associated with Tuple Sketch
The FiniteFirehoseFactory and InputRowParser classes were deprecated in 0.17.0 (#8823) in favor of InputSource & InputFormat. This PR removes the FiniteFirehoseFactory and all its implementations along with classes solely used by them like Fetcher (Used by PrefetchableTextFilesFirehoseFactory). Refactors classes including tests using FiniteFirehoseFactory to use InputSource instead.
Removing InputRowParser may not be as trivial as many classes that aren't deprecated depends on it (with no alternatives), like EventReceiverFirehoseFactory. Hence FirehoseFactory, EventReceiverFirehoseFactory, and Firehose are marked deprecated.
*When running REPLACE queries, the segments which contain no data are dropped (marked as unused). This PR aims to generate tombstones in place of segments which contain no data to mark their deletion, as is the behavior with the native ingestion.
This will cause InsertCannotReplaceExistingSegmentFault to be removed since it was generated if the interval to be marked unused didn't fully overlap one of the existing segments to replace.
* Fix NPE in KinesisSupervisor#setupRecordSupplier.
PR #13539 refactored record supplier creation and introduced a bug:
this method would throw NPE when recordsPerFetch was not provided
by the user. recordsPerFetch isn't needed in this context at all,
since the supervisor-side supplier doesn't fetch records. So this
patch sets it to zero.
* Remove unused imports.
If the intermediate handoff period is less than the task duration and there is no new data in the input topic, task will continuously checkpoint the same offsets again and again. This PR fixes that bug by resetting the checkpoint time even when the task receives the same end offset request again.
* merge druid-core, extendedset, and druid-hll into druid-processing to simplify everything
* fix poms and license stuff
* mockito is evil
* allow reset of JvmUtils RuntimeInfo if tests used static injection to override
* Use an HllSketchHolder object to enable optimized merge
HllSketchAggregatorFactory.combine had been implemented using a
pure pair-wise, "make a union -> add 2 things to union -> get sketch"
algorithm. This algorithm does 2 things that was CPU
1) The Union object always builds an HLL_8 sketch regardless of the
target type. This means that when the target type is not HLL_8, we
spent CPU cycles converting to HLL_8 and back over and over again
2) By throwing away the Union object and converting back to the
HllSketch only to build another Union object, we do lots and lots
of copy+conversions of the HllSketch
This change introduces an HllSketchHolder object which can hold onto
a Union object and delay conversion back into an HllSketch until
it is actually needed. This follows the same pattern as the
SketchHolder object for theta sketches.
changes:
* modified druid schema column type compution to special case COMPLEX<json> handling to choose COMPLEX<json> if any column in any segment is COMPLEX<json>
* NestedFieldVirtualColumn can now work correctly on any type of column, returning either a column selector if a root path, or nil selector if not
* fixed a random bug with NilVectorSelector when using a vector size larger than the default and druid.generic.useDefaultValueForNull=false would have the nulls vector set to all false instead of true
* fixed an overly aggressive check in ExprEval.ofType when handling complex types which would try to treat any string as base64 without gracefully falling back if it was not in fact base64 encoded, along with special handling for complex<json>
* added ExpressionVectorSelectors.castValueSelectorToObject and ExpressionVectorSelectors.castObjectSelectorToNumeric as convience methods to cast vector selectors using cast expressions without the trouble of constructing an expression. the polymorphic nature of the non-vectorized engine (and significantly larger overhead of non-vectorized expression processing) made adding similar methods for non-vectorized selectors less attractive and so have not been added at this time
* fix inconsistency between nested column indexer and serializer in handling values (coerce non primitive and non arrays of primitives using asString)
* ExprEval best effort mode now handles byte[] as string
* added test for ExprEval.bestEffortOf, and add missing conversion cases that tests uncovered
* more tests more better
* Fallback virtual column
This virtual columns enables falling back to another column if
the original column doesn't exist. This is useful when doing
column migrations and you have some old data with column X,
new data with column Y and you want to use Y if it exists, X
otherwise so that you can run a consistent query against all of
the data.
With fault tolerance enabled in MSQ, not all the work orders might be populated if the worker is restarted. In case it gets the request for cleaning up the stage which is not present in the worker's map, it can throw an NPE. Added a check to ensure that the stage is present in the map before cleaning it up, or else logging it as a warning.
* SQL test framework extensions
* Capture planner artifacts: logical plan, etc.
* Planner test builder validates the logical plan
* Validation for the SQL resut schema (we already have
validation for the Druid row signature)
* Better Guice integration: properties, reuse Guice modules
* Avoid need for hand-coded expr, macro tables
* Retire some of the test-specific query component creation
* Fix query log hook race condition
Co-authored-by: Paul Rogers <progers@apache.org>
* discover nested columns when using nested column indexer for schemaless
* move useNestedColumnIndexerForSchemaDiscovery from AppendableIndexSpec to DimensionsSpec
Much improved table functions
* Revises properties, definitions in the catalog
* Adds a "table function" abstraction to model such functions
* Specific functions for HTTP, inline, local and S3.
* Extended SQL types in the catalog
* Restructure external table definitions to use table functions
* EXTEND syntax for Druid's extern table function
* Support for array-valued table function parameters
* Support for array-valued SQL query parameters
* Much new documentation
* Kinesis: More robust default fetch settings.
1) Default recordsPerFetch and recordBufferSize based on available memory
rather than using hardcoded numbers. For this, we need an estimate
of record size. Use 10 KB for regular records and 1 MB for aggregated
records. With 1 GB heaps, 2 processors per task, and nonaggregated
records, recordBufferSize comes out to the same as the old
default (10000), and recordsPerFetch comes out slightly lower (1250
instead of 4000).
2) Default maxRecordsPerPoll based on whether records are aggregated
or not (100 if not aggregated, 1 if aggregated). Prior default was 100.
3) Default fetchThreads based on processors divided by task count on
Indexers, rather than overall processor count.
4) Additionally clean up the serialized JSON a bit by adding various
JsonInclude annotations.
* Updates for tests.
* Additional important verify.
* Quote and escape table, key and column names.
* fix typo.
* More select statements.
* Derby lookup tests create quoted identifiers so it's compatible.
* Use Stringutils.replace() utility.
* quote the filter string.
* Squish doubly quote usage into a single function.
* Add parameterized test with reserved identifiers.
* few changes.
* Addition of NaiveSortMaker and Default implementation
Add the NaiveSortMaker which makes a sorter
object and a default implementation of the
interface.
This also allows us to plan multiple different window
definitions on the same query.
* Validate response headers and fix exception logging
A class of QueryException were throwing away their
causes making it really hard to determine what's
going wrong when something goes wrong in the SQL
planner specifically. Fix that and adjust tests
to do more validation of response headers as well.
We allow 404s and 307s to be returned even without
authorization validated, but others get converted to 403
This PR expands `StringDimensionIndexer` to handle conversion of `byte[]` to base64 encoded strings, rather than the current behavior of calling java `toString`.
This issue was uncovered by a regression of sorts introduced by #13519, which updated the protobuf extension to directly convert stuff to java types, resulting in `bytes` typed values being converted as `byte[]` instead of a base64 string which the previous JSON based conversion created. While outputting `byte[]` is more consistent with other input formats, and preferable when the bytes can be consumed directly (such as complex types serde), when fed to a `StringDimensionIndexer`, it resulted in an ugly java `toString` because `processRowValsToUnsortedEncodedKeyComponent` is fed the output of `row.getRaw(..)`. Converting `byte[]` to a base64 string within `StringDimensionIndexer` is consistent with the behavior of calling `row.getDimension(..)` which does do this coercion (and why many tests on binary types appeared to be doing the expected thing).
I added some protobuf `bytes` tests, but they don't really hit the new `StringDimensionIndexer` behavior because they operate on the `InputRow` directly, and call `getDimension` to validate stuff. The parser based version still uses the old conversion mechanisms, so when not using a flattener incorrectly calls `toString` on the `ByteString`. I have encoded this behavior in the test for now, if we either update the parser to use the new flattener or just .. remove parsers we can remove this test stuff.
Follow up to #13520
Bytes processed are currently tracked for intermediate stages in MSQ ingestion.
This patch adds the capability to track the bytes processed by an MSQ controller
task while reading from an external input source or a segment source.
Changes:
- Track `processedBytes` for every `InputSource` read in `ExternalInputSliceReader`
- Update `ChannelCounters` with the above obtained `processedBytes` when incrementing
the input file count.
- Update task report structure in docs
The total input processed bytes can be obtained by summing the `processedBytes` as follows:
totalBytes = 0
for every root stage (i.e. a stage which does not have another stage as an input):
for every worker in that stage:
for every input channel: (i.e. channels with prefix "input", e.g. "input0", "input1", etc.)
totalBytes += processedBytes
* Add validation checks to worker chat handler apis
* Merge things and polishing the error messages.
* Minor error message change
* Fixing race and adding some tests
* Fixing controller fetching stats from wrong workers.
Fixing race
Changing default mode to Parallel
Adding logging.
Fixing exceptions not propagated properly.
* Changing to kernel worker count
* Added a better logic to figure out assigned worker for a stage.
* Nits
* Moving to existing kernel methods
* Adding more coverage
Co-authored-by: cryptoe <karankumar1100@gmail.com>
This commit adds a new class `InputStats` to track the total bytes processed by a task.
The field `processedBytes` is published in task reports along with other row stats.
Major changes:
- Add class `InputStats` to track processed bytes
- Add method `InputSourceReader.read(InputStats)` to read input rows while counting bytes.
> Since we need to count the bytes, we could not just have a wrapper around `InputSourceReader` or `InputEntityReader` (the way `CountableInputSourceReader` does) because the `InputSourceReader` only deals with `InputRow`s and the byte information is already lost.
- Classic batch: Use the new `InputSourceReader.read(inputStats)` in `AbstractBatchIndexTask`
- Streaming: Increment `processedBytes` in `StreamChunkParser`. This does not use the new `InputSourceReader.read(inputStats)` method.
- Extend `InputStats` with `RowIngestionMeters` so that bytes can be exposed in task reports
Other changes:
- Update tests to verify the value of `processedBytes`
- Rename `MutableRowIngestionMeters` to `SimpleRowIngestionMeters` and remove duplicate class
- Replace `CacheTestSegmentCacheManager` with `NoopSegmentCacheManager`
- Refactor `KafkaIndexTaskTest` and `KinesisIndexTaskTest`
Refactor DataSource to have a getAnalysis method()
This removes various parts of the code where while loops and instanceof
checks were being used to walk through the structure of DataSource objects
in order to build a DataSourceAnalysis. Instead we just ask the DataSource
for its analysis and allow the stack to rebuild whatever structure existed.
* Zero-copy local deep storage.
This is useful for local deep storage, since it reduces disk usage and
makes Historicals able to load segments instantaneously.
Two changes:
1) Introduce "druid.storage.zip" parameter for local storage, which defaults
to false. This changes default behavior from writing an index.zip to writing
a regular directory. This is safe to do even during a rolling update, because
the older code actually already handled unzipped directories being present
on local deep storage.
2) In LocalDataSegmentPuller and LocalDataSegmentPusher, use hard links
instead of copies when possible. (Generally this is possible when the
source and destination directory are on the same filesystem.)
The planner sets sqlInsertSegmentGranularity in its context when using
PARTITIONED BY, which sets it on every native query in the stack (as all
native queries for a SQL query typically have the same context).
QueryKit would interpret that as a request to configure bucketing for
all native queries. This isn't useful, as bucketing is only used for
the penultimate stage in INSERT / REPLACE.
So, this patch modifies QueryKit to only look at sqlInsertSegmentGranularity
on the outermost query.
As an additional change, this patch switches the static ObjectMapper to
use the processwide ObjectMapper for deserializing Granularities. Saves
an ObjectMapper instance, and ensures that if there are any special
serdes registered for Granularity, we'll pick them up.
1) Edited the TooManyBuckets error message to mention PARTITIONED BY
instead of segmentGranularity.
2) Added error-code-specific anchors in the docs.
3) Add information to various error codes in the docs about common
causes and solutions.
* Remove stray reference to fix OOM while merging sketches
* Update future to add result from executor service
* Update tests and address review comments
* Address review comments
* Moved mock
* Close threadpool on teardown
* Remove worker task cancel
SQL test framework extensions
* Capture planner artifacts: logical plan, etc.
* Planner test builder validates the logical plan
* Validation for the SQL resut schema (we already have
validation for the Druid row signature)
* Better Guice integration: properties, reuse Guice modules
* Avoid need for hand-coded expr, macro tables
* Retire some of the test-specific query component creation
* Fix query log hook race condition
* add faults tests for the multi stage query
* add too many parttiions fault
* add toomanyinputfilesfault
* programmatically generate the file
* refactor
* Trigger Build
https://github.com/apache/druid/pull/13027 PR replaces `filter` parameter with
`objectGlob` in ingestion input source. However, this will cause existing ingestion
jobs to fail if they are using a filter already. This PR adds old filter functionality
alongside objectGlob to preserve backward compatibility.
* we can read where we want to
we can leave your bounds behind
'cause if the memory is not there
we really don't care
and we'll crash this process of mine
* Attach IO error to parse error when we can't contact Avro schema registry.
The change in #12080 lost the original exception context. This patch
adds it back.
* Add hamcrest-core.
* Fix format string.
Main changes:
1) Convert SeekableStreamIndexTaskClient to an interface, move old code
to SeekableStreamIndexTaskClientSyncImpl, and add new implementation
SeekableStreamIndexTaskClientAsyncImpl that uses ServiceClient.
2) Add "chatAsync" parameter to seekable stream supervisors that causes
the supervisor to use an async task client.
3) In SeekableStreamSupervisor.discoverTasks, adjust logic to avoid making
blocking RPC calls in workerExec threads.
4) In SeekableStreamSupervisor generally, switch from Futures.successfulAsList
to FutureUtils.coalesce, so we can better capture the errors that occurred
with contacting individual tasks.
Other, related changes:
1) Add ServiceRetryPolicy.retryNotAvailable, which controls whether
ServiceClient retries unavailable services. Useful since we do not
want to retry calls unavailable tasks within the service client. (The
supervisor does its own higher-level retries.)
2) Add FutureUtils.transformAsync, a more lambda friendly version of
Futures.transform(f, AsyncFunction).
3) Add FutureUtils.coalesce. Similar to Futures.successfulAsList, but
returns Either instead of using null on error.
4) Add JacksonUtils.readValue overloads for JavaType and TypeReference.
Fixes inclusion of all stream partitions in all tasks.
The PR (Adds Idle feature to `SeekableStreamSupervisor` for inactive stream) - https://github.com/apache/druid/pull/13144 updates the resulting lag calculation map in `KafkaSupervisor` to include all the latest partitions from the stream to set the idle state accordingly rather than the previous way of lag calculation only for the partitions actively being read from the stream. This led to an explosion of metrics in lag reports in cases where 1000s of tasks per supervisor are present.
Changes:
- Add a new method to generate lags for only those partitions a single task is actively reading from while updating the Supervisor reports.
Druid catalog basics
Catalog object model for tables, columns
Druid metadata DB storage (as an extension)
REST API to update the catalog (as an extension)
Integration tests
Model only: no planner integration yet
* Use standard library to correctly glob and stop at the correct folder structure when filtering cloud objects.
Removed:
import org.apache.commons.io.FilenameUtils;
Add:
import java.nio.file.FileSystems;
import java.nio.file.PathMatcher;
import java.nio.file.Paths;
* Forgot to update CloudObjectInputSource as well.
* Fix tests.
* Removed unused exceptions.
* Able to reduced user mistakes, by removing the protocol and the bucket on filter.
* add 1 more test.
* add comment on filterWithoutProtocolAndBucket
* Fix lint issue.
* Fix another lint issue.
* Replace all mention of filter -> objectGlob per convo here:
https://github.com/apache/druid/pull/13027#issuecomment-1266410707
* fix 1 bad constructor.
* Fix the documentation.
* Don’t do anything clever with the object path.
* Remove unused imports.
* Fix spelling error.
* Fix incorrect search and replace.
* Addressing Gian’s comment.
* add filename on .spelling
* Fix documentation.
* fix documentation again
Co-authored-by: Didip Kerabat <didip@apple.com>
* scratch
* s3 ls fix, add docs
* add documentation, update method name
* Add tests, address commits, change default value of the helper
* fix test
* update the default value of config, remove initial delay config
* Trigger Build
* update class
* add more tests
* docs update
* spellcheck
* remove ioe from the signature
* add back dmmy constructor for initialization
* fix guice bindings, intellij inspections
* MSQ: Fix task lock checking during publish, fix lock priority.
Fixes two issues:
1) ControllerImpl did not properly check the return value of
SegmentTransactionalInsertAction when doing a REPLACE. This could cause
it to not realize that its locks were preempted.
2) Task lock priority was the default of 0. It should be the higher
batch default of 50. The low priority made it possible for MSQ tasks
to be preempted by compaction tasks, which is not desired.
* Restructuring, add docs.
* Add performSegmentPublish tests.
* Fix tests.
* MSQ: Consider PARTITION_STATS_MAX_BYTES in WorkerMemoryParameters.
This consideration is important, because otherwise we can run out of
memory due to large statistics-tracking objects.
* Improved calculations.
* Always return sketches from DS_HLL, DS_THETA, DS_QUANTILES_SKETCH.
These aggregation functions are documented as creating sketches. However,
they are planned into native aggregators that include finalization logic
to convert the sketch to a number of some sort. This creates an
inconsistency: the functions sometimes return sketches, and sometimes
return numbers, depending on where they lie in the native query plan.
This patch changes these SQL aggregators to _never_ finalize, by using
the "shouldFinalize" feature of the native aggregators. It already
existed for theta sketches. This patch adds the feature for hll and
quantiles sketches.
As to impact, Druid finalizes aggregators in two cases:
- When they appear in the outer level of a query (not a subquery).
- When they are used as input to an expression or finalizing-field-access
post-aggregator (not any other kind of post-aggregator).
With this patch, the functions will no longer be finalized in these cases.
The second item is not likely to matter much. The SQL functions all declare
return type OTHER, which would be usable as an input to any other function
that makes sense and that would be planned into an expression.
So, the main effect of this patch is the first item. To provide backwards
compatibility with anyone that was depending on the old behavior, the
patch adds a "sqlFinalizeOuterSketches" query context parameter that
restores the old behavior.
Other changes:
1) Move various argument-checking logic from runtime to planning time in
DoublesSketchListArgBaseOperatorConversion, by adding an OperandTypeChecker.
2) Add various JsonIgnores to the sketches to simplify their JSON representations.
3) Allow chaining of ExpressionPostAggregators and other PostAggregators
in the SQL layer.
4) Avoid unnecessary FieldAccessPostAggregator wrapping in the SQL layer,
now that expressions can operate on complex inputs.
5) Adjust return type to thetaSketch (instead of OTHER) in
ThetaSketchSetBaseOperatorConversion.
* Fix benchmark class.
* Fix compilation error.
* Fix ThetaSketchSqlAggregatorTest.
* Hopefully fix ITAutoCompactionTest.
* Adjustment to ITAutoCompactionTest.
* Use lookup memory footprint in MSQ memory computations.
Two main changes:
1) Add estimateHeapFootprint to LookupExtractor.
2) Use this in MSQ's IndexerWorkerContext when determining the total
amount of available memory. It's taken off the top.
This prevents MSQ tasks from running out of memory when there are lookups
defined in the cluster.
* Updates from code review.
* Conversion from taskId to workerNumber in the workerClient
* storage connector changes, suffix file when finish writing to it
* Fix tests
* Trigger Build
* convert IntFunction to a dedicated interface
* first review round
* use a dummy file to indicate success
* fetch the first filename from the list in case of multiple files
* tests working, fix semantic issue with ls
* change how the success flag works
* comments, checkstyle, method rename
* fix test
* forbiddenapis fix
* Trigger Build
* change the writer
* dead store fix
* Review comments
* revert changes
* review
* review comments
* Update extensions-core/multi-stage-query/src/main/java/org/apache/druid/msq/shuffle/DurableStorageInputChannelFactory.java
Co-authored-by: Karan Kumar <karankumar1100@gmail.com>
* Update extensions-core/multi-stage-query/src/main/java/org/apache/druid/msq/shuffle/DurableStorageInputChannelFactory.java
Co-authored-by: Karan Kumar <karankumar1100@gmail.com>
* update error messages
* better error messages
* fix checkstyle
Co-authored-by: Karan Kumar <karankumar1100@gmail.com>
* Support for middle manager less druid, tasks launch as k8s jobs
* Fixing forking task runner test
* Test cleanup, dependency cleanup, intellij inspections cleanup
* Changes per PR review
Add configuration option to disable http/https proxy for the k8s client
Update the docs to provide more detail about sidecar support
* Removing un-needed log lines
* Small changes per PR review
* Upon task completion we callback to the overlord to update the status / locaiton, for slower k8s clusters, this reduces locking time significantly
* Merge conflict fix
* Fixing tests and docs
* update tiny-cluster.yaml
changed `enableTaskLevelLogPush` to `encapsulatedTask`
* Apply suggestions from code review
Co-authored-by: Abhishek Agarwal <1477457+abhishekagarwal87@users.noreply.github.com>
* Minor changes per PR request
* Cleanup, adding test to AbstractTask
* Add comment in peon.sh
* Bumping code coverage
* More tests to make code coverage happy
* Doh a duplicate dependnecy
* Integration test setup is weird for k8s, will do this in a different PR
* Reverting back all integration test changes, will do in anotbher PR
* use StringUtils.base64 instead of Base64
* Jdk is nasty, if i compress in jdk 11 in jdk 17 the decompressed result is different
Co-authored-by: Rahul Gidwani <r_gidwani@apple.com>
Co-authored-by: Abhishek Agarwal <1477457+abhishekagarwal87@users.noreply.github.com>
In clusters with a large number of segments, the duty `MarkAsUnusedOvershadowedSegments`
can take a long very long time to finish. This is because of the costly invocation of
`timeline.isOvershadowed` which is done for every used segment in every coordinator run.
Changes
- Use `DataSourceSnapshot.getOvershadowedSegments` to get all overshadowed segments
- Iterate over this set instead of all used segments to identify segments that can be marked as unused
- Mark segments as unused in the DB in batches rather than one at a time
- Refactor: Add class `SegmentTimeline` for ease of use and readability while using a
`VersionedIntervalTimeline` of segments.
* introduce a "tree" type to the flattenSpec
* feedback - rename exprs to nodes, use CollectionsUtils.isNullOrEmpty for guard
* feedback - expand docs to more clearly capture limitations of "tree" flattenSpec
* feedback - fix for typo on docs
* introduce a comment to explain defensive copy, tweak null handling
* fix: part of rebase
* mark ObjectFlatteners.FlattenerMaker as an ExtensionPoint and provide default for new tree type
* fix: objectflattener restore previous behavior to call getRootField for root type
* docs: ingestion/data-formats add note that ORC only supports path expressions
* chore: linter remove unused import
* fix: use correct newer form for empty DimensionsSpec in FlattenJSONBenchmark
Fixes a problem where, due to the inexactness of floating-point math, we
would potentially drift while tracking retained byte counts and run into
assertion failures in assertRetainedByteCountsAreTrackedCorrectly.
* First set of changes for framework
* Second set of changes to move segment map function to data source
* Minot change to server manager
* Removing the createSegmentMapFunction from JoinableFactoryWrapper and moving to JoinDataSource
* Checkstyle fixes
* Patching Eric's fix for injection
* Checkstyle and fixing some CI issues
* Fixing code inspections and some failed tests and one injector for test in avatica
* Another set of changes for CI...almost there
* Equals and hashcode part update
* Fixing injector from Eric + refactoring for broadcastJoinHelper
* Updating second injector. Might revert later if better way found
* Fixing guice issue in JoinableFactory
* Addressing review comments part 1
* Temp changes refactoring
* Revert "Temp changes refactoring"
This reverts commit 9da42a9ef0.
* temp
* Temp discussions
* Refactoring temp
* Refatoring the query rewrite to refer to a datasource
* Refactoring getCacheKey by moving it inside data source
* Nullable annotation check in injector
* Addressing some comments, removing 2 analysis.isJoin() checks and correcting the benchmark files
* Minor changes for refactoring
* Addressing reviews part 1
* Refactoring part 2 with new test cases for broadcast join
* Set for nullables
* removing instance of checks
* Storing nullables in guice to avoid checking on reruns
* Fixing a test case and removing an irrelevant line
* Addressing the atomic reference review comments
* Remove basePersistDirectory from tuning configs.
Since the removal of CliRealtime, it serves no purpose, since it is
always overridden in production using withBasePersistDirectory given
some subdirectory of the task work directory.
Removing this from the tuning config has a benefit beyond removing
no-longer-needed logic: it also avoids the side effect of empty
"druid-realtime-persist" directories getting created in the systemwide
temp directory.
* Test adjustments to appropriately set basePersistDirectory.
* Remove unused import.
* Fix RATC constructor.
* Refactor Calcite test "framework" for planner tests
Refactors the current Calcite tests to make it a bit easier
to adjust the set of runtime objects used within a test.
* Move data creation out of CalciteTests into TestDataBuilder
* Move "framework" creation out of CalciteTests into
a QueryFramework
* Move injector-dependent functions from CalciteTests
into QueryFrameworkUtils
* Wrapper around the planner factory, etc. to allow
customization.
* Bulk of the "framework" created once per class rather
than once per test.
* Refactor tests to use a test builder
* Change all testQuery() methods to use the test builder.
Move test execution & verification into a test runner.
In MSQ, there can be an upper limit to the number of worker warnings. For example, for parseExceptions encountered while parsing the external data, the user can specify an upper limit to the number of parse exceptions that can be allowed before it throws an error of type TooManyWarnings.
This PR makes it so that if the user disallows warnings of a certain type i.e. the limit is 0 (or is executing in strict mode), instead of throwing an error of type TooManyWarnings, we can directly surface the warning as the error, saving the user from the hassle of going throw the warning reports.
* SQL: Use timestamp_floor when granularity is not safe.
PR #12944 added a check at the execution layer to avoid materializing
excessive amounts of time-granular buckets. This patch modifies the SQL
planner to avoid generating queries that would throw such errors, by
switching certain plans to use the timestamp_floor function instead of
granularities. This applies both to the Timeseries query type, and the
GroupBy timestampResultFieldGranularity feature.
The patch also goes one step further: we switch to timestamp_floor
not just in the ETERNITY + non-ALL case, but also if the estimated
number of time-granular buckets exceeds 100,000.
Finally, the patch modifies the timestampResultFieldGranularity
field to consistently be a String rather than a Granularity. This
ensures that it can be round-trip serialized and deserialized, which is
useful when trying to execute the results of "EXPLAIN PLAN FOR" with
GroupBy queries that use the timestampResultFieldGranularity feature.
* Fix test, address PR comments.
* Fix ControllerImpl.
* Fix test.
* Fix unused import.
We introduce two new configuration keys that refine the query context security model controlled by druid.auth.authorizeQueryContextParams. When that value is set to true then two other configuration options become available:
druid.auth.unsecuredContextKeys: The set of query context keys that do not require a security check. Use this for the "white-list" of key to allow. All other keys go through the existing context key security checks.
druid.auth.securedContextKeys: The set of query context keys that do require a security check. Use this when you want to allow all but a specific set of keys: only these keys go through the existing context key security checks.
Both are set using JSON list format:
druid.auth.securedContextKeys=["secretKey1", "secretKey2"]
You generally set one or the other values. If both are set, unsecuredContextKeys acts as exceptions to securedContextKeys.
In addition, Druid defines two query context keys which always bypass checks because Druid uses them internally:
sqlQueryId
sqlStringifyArrays
It was found that the namespace/cache/heapSizeInBytes metric that tracks the total heap size in bytes of all lookup caches loaded on a service instance was being under reported. We were not accounting for the memory overhead of the String object, which I've found in testing to be ~40 bytes. While this overhead may be java version dependent, it should not vary much, and accounting for this provides a better estimate. Also fixed some logging, and reading bytes from the JDBI result set a little more efficient by saving hash table lookups. Also added some of the lookup metrics to the default statsD emitter metric whitelist.
* Converted Druid planner to use statement handlers
Converts the large collection of if-statements for statement
types into a set of classes: one per supported statement type.
Cleans up a few error messages.
* Revisions from review comments
* Build fix
* Build fix
* Resolve merge confict.
* More merges with QueryResponse PR
* More parameterized type cleanup
Forces a rebuild due to a flaky test
* Cleaner JSON for various input sources and formats.
Add JsonInclude to various properties, to avoid population of default
values in serialized JSON.
Also fixes a bug in OrcInputFormat: it was not writing binaryAsString,
so the property would be lost on serde.
* Additonal test cases.
* Expose HTTP Response headers from SqlResource
This change makes the SqlResource expose HTTP response
headers in the same way that the QueryResource exposes them.
Fundamentally, the change is to pipe the QueryResponse
object all the way through to the Resource so that it can
populate response headers. There is also some code
cleanup around DI, as there was a superfluous FactoryFactory
class muddying things up.
* MSQ extension: Fix over-capacity write in ScanQueryFrameProcessor.
Frame processors are meant to write only one output frame per cycle.
The ScanQueryFrameProcessor would write two when reading from a channel
if the input frame cursor cycled and then the output frame filled up
while reading from the next frame.
This patch fixes the bug, and adds a test. It also makes some adjustments
to the processor code in order to make it easier to test.
* Add license header.
* more consistent expression error messages
* review stuff
* add NamedFunction for Function, ApplyFunction, and ExprMacro to share common stuff
* fixes
* add expression transform name to transformer failure, better parse_json error messaging
* KLL sketch
* added documentation
* direct static refs
* direct static refs
* fixed test
* addressed review points
* added KLL sketch related terms
* return a copy from get
* Copy unions when returning them from "get".
* Remove redundant "final".
Co-authored-by: AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com>
Co-authored-by: Gian Merlino <gianmerlino@gmail.com>
* Fixing RACE in HTTP remote task Runner
* Changes in the interface
* Updating documentation
* Adding test cases to SwitchingTaskLogStreamer
* Adding more tests
This commit is a first draft of the revised integration test framework which provides:
- A new directory, integration-tests-ex that holds the new integration test structure. (For now, the existing integration-tests is left unchanged.)
- Maven module druid-it-tools to hold code placed into the Docker image.
- Maven module druid-it-image to build the Druid-only test image from the tarball produced in distribution. (Dependencies live in their "official" image.)
- Maven module druid-it-cases that holds the revised tests and the framework itself. The framework includes file-based test configuration, test-specific clients, test initialization and updated versions of some of the common test support classes.
The integration test setup is primarily a huge mass of details. This approach refactors many of those details: from how the image is built and configured to how the Docker Compose scripts are structured to test configuration. An extensive set of "readme" files explains those details. Rather than repeat that material here, please consult those files for explanations.
The Avro parsing code leaks some "object" representations.
We need to convert them into Maps/Lists so that other code
can understand and expect good things. Previously, these
objects were handled with .toString(), but that's not a
good contract in terms of how to work with objects.
* Refactor SqlLifecycle into statement classes
Create direct & prepared statements
Remove redundant exceptions from tests
Tidy up Calcite query tests
Make PlannerConfig more testable
* Build fixes
* Added builder to SqlQueryPlus
* Moved Calcites system properties to saffron.properties
* Build fix
* Resolve merge conflict
* Fix IntelliJ inspection issue
* Revisions from reviews
Backed out a revision to Calcite tests that didn't work out as planned
* Build fix
* Fixed spelling errors
* Fixed failed test
Prepare now enforces security; before it did not.
* Rebase and fix IntelliJ inspections issue
* Clean up exception handling
* Fix handling of JDBC auth errors
* Build fix
* More tweaks to security messages
In the current druid code base, we have the interface DataSegmentPusher which allows us to push segments to the appropriate deep storage without the extension being worried about the semantics of how to push too deep storage.
While working on #12262, whose some part of the code will go as an extension, I realized that we do not have an interface that allows us to do basic "write, get, delete, deleteAll" operations on the appropriate deep storage without let's say pulling the s3-storage-extension dependency in the custom extension.
Hence, the idea of StorageConnector was born where the storage connector sits inside the druid core so all extensions have access to it.
Each deep storage implementation, for eg s3, GCS, will implement this interface.
Now with some Jackson magic, we bind the implementation of the correct deep storage implementation on runtime using a type variable.
* change kafka lookups module to not commit offsets
The current behaviour of the Kafka lookup extractor is to not commit
offsets by assigning a unique ID to the consumer group and setting
auto.offset.reset to earliest. This does the job but also pollutes the
Kafka broker with a bunch of "ghost" consumer groups that will never again be
used.
To fix this, we now set enable.auto.commit to false, which prevents the
ghost consumer groups being created in the first place.
* update docs to include new enable.auto.commit setting behaviour
* update kafka-lookup-extractor documentation
Provide some additional detail on functionality and configuration.
Hopefully this will make it clearer how the extractor works for
developers who aren't so familiar with Kafka.
* add comments better explaining the logic of the code
* add spelling exceptions for kafka lookup docs
* remove kafka lookup records from factory when record tombstoned
* update kafka lookup docs to include tombstone behaviour
* change test wait time down to 10ms
Co-authored-by: David Palmer <david.palmer@adscale.co.nz>
Kinesis ingestion requires all shards to have at least 1 record at the required position in druid.
Even if this is satisified initially, resharding the stream can lead to empty intermediate shards. A significant delay in writing to newly created shards was also problematic.
Kinesis shard sequence numbers are big integers. Introduce two more custom sequence tokens UNREAD_TRIM_HORIZON and UNREAD_LATEST to indicate that a shard has not been read from and that it needs to be read from the start or the end respectively.
These values can be used to avoid the need to read at least one record to obtain a sequence number for ingesting a newly discovered shard.
If a record cannot be obtained immediately, use a marker to obtain the relevant shardIterator and use this shardIterator to obtain a valid sequence number. As long as a valid sequence number is not obtained, continue storing the token as the offset.
These tokens (UNREAD_TRIM_HORIZON and UNREAD_LATEST) are logically ordered to be earlier than any valid sequence number.
However, the ordering requires a few subtle changes to the existing mechanism for record sequence validation:
The sequence availability check ensures that the current offset is before the earliest available sequence in the shard. However, current token being an UNREAD token indicates that any sequence number in the shard is valid (despite the ordering)
Kinesis sequence numbers are inclusive i.e if current sequence == end sequence, there are more records left to read.
However, the equality check is exclusive when dealing with UNREAD tokens.
* Refactor Guice initialization
Builders for various module collections
Revise the extensions loader
Injector builders for server startup
Move Hadoop init to indexer
Clean up server node role filtering
Calcite test injector builder
* Revisions from review comments
* Build fixes
* Revisions from review comments
* Improved Java 17 support and Java runtime docs.
1) Add a "Java runtime" doc page with information about supported
Java versions, garbage collection, and strong encapsulation..
2) Update asm and equalsverifier to versions that support Java 17.
3) Add additional "--add-opens" lines to surefire configuration, so
tests can pass successfully under Java 17.
4) Switch openjdk15 tests to openjdk17.
5) Update FrameFile to specifically mention Java runtime incompatibility
as the cause of not being able to use Memory.map.
6) Update SegmentLoadDropHandler to log an error for Errors too, not
just Exceptions. This is important because an IllegalAccessError is
encountered when the correct "--add-opens" line is not provided,
which would otherwise be silently ignored.
7) Update example configs to use druid.indexer.runner.javaOptsArray
instead of druid.indexer.runner.javaOpts. (The latter is deprecated.)
* Adjustments.
* Use run-java in more places.
* Add run-java.
* Update .gitignore.
* Exclude hadoop-client-api.
Brought in when building on Java 17.
* Swap one more usage of java.
* Fix the run-java script.
* Fix flag.
* Include link to Temurin.
* Spelling.
* Update examples/bin/run-java
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
Historicals and middle managers crash with an `UnknownHostException` on trying
to load `druid-parquet-extensions` with an ephemeral Hadoop cluster. This happens
because the `fs.defaultFS` URI value cannot be resolved at start up time as the
hadoop cluster may not exist at startup time.
This commit fixes the error by performing initialization of the filesystem in
`ParquetInputFormat.createReader()` whenever a new reader is requested.
* fix bug in ObjectFlatteners.toMap which caused null values in avro-stream/avro-ocf/parquet/orc to be converted to {} instead of null
* fix parquet test that expected wrong behavior, my bad heh
* Mid-level service client and updated high-level clients.
Our servers talk to each other over HTTP. We have a low-level HTTP
client (HttpClient) that is super-asynchronous and super-customizable
through its handlers. It's also proven to be quite robust: we use it
for Broker -> Historical communication over the wide variety of query
types and workloads we support.
But the low-level client has no facilities for service location or
retries, which means we have a variety of high-level clients that
implement these in their own ways. Some high-level clients do a better
job than others. This patch adds a mid-level ServiceClient that makes
it easier for high-level clients to be built correctly and harmoniously,
and migrates some of the high-level logic to use ServiceClients.
Main changes:
1) Add ServiceClient org.apache.druid.rpc package. That package also
contains supporting stuff like ServiceLocator and RetryPolicy
interfaces, and a DiscoveryServiceLocator based on
DruidNodeDiscoveryProvider.
2) Add high-level OverlordClient in org.apache.druid.rpc.indexing.
3) Indexing task client creator in TaskServiceClients. It uses
SpecificTaskServiceLocator to find the tasks. This improves on
ClientInfoTaskProvider by caching task locations for up to 30 seconds
across calls, reducing load on the Overlord.
4) Rework ParallelIndexSupervisorTaskClient to use a ServiceClient
instead of extending IndexTaskClient.
5) Rework RemoteTaskActionClient to use a ServiceClient instead of
DruidLeaderClient.
6) Rework LocalIntermediaryDataManager, TaskMonitor, and
ParallelIndexSupervisorTask. As a result, MiddleManager, Peon, and
Overlord no longer need IndexingServiceClient (which internally used
DruidLeaderClient).
There are some concrete benefits over the prior logic, namely:
- DruidLeaderClient does retries in its "go" method, but only retries
exactly 5 times, does not sleep between retries, and does not retry
retryable HTTP codes like 502, 503, 504. (It only retries IOExceptions.)
ServiceClient handles retries in a more reasonable way.
- DruidLeaderClient's methods are all synchronous, whereas ServiceClient
methods are asynchronous. This is used in one place so far: the
SpecificTaskServiceLocator, so we don't need to block a thread trying
to locate a task. It can be used in other places in the future.
- HttpIndexingServiceClient does not properly handle all server errors.
In some cases, it tries to parse a server error as a successful
response (for example: in getTaskStatus).
- IndexTaskClient currently makes an Overlord call on every task-to-task
HTTP request, as a way to find where the target task is. ServiceClient,
through SpecificTaskServiceLocator, caches these target locations
for a period of time.
* Style adjustments.
* For the coverage.
* Adjustments.
* Better behaviors.
* Fixes.
* Fix flaky KafkaIndexTaskTest.
The testRunTransactionModeRollback case had many race conditions. Most notably,
it would commit a transaction and then immediately check to see that the results
were *not* indexed. This is racey because it relied on the indexing thread being
slower than the test thread.
Now, the case waits for the transaction to be processed by the indexing thread
before checking the results.
* Changes from review.
In a heterogeneous environment, sometimes you don't have control over the input folder. Upstream can put any folder they want. In this situation the S3InputSource.java is unusable.
Most people like me solved it by using Airflow to fetch the full list of parquet files and pass it over to Druid. But doing this explodes the JSON spec. We had a situation where 1 of the JSON spec is 16MB and that's simply too much for Overlord.
This patch allows users to pass {"filter": "*.parquet"} and let Druid performs the filtering of the input files.
I am using the glob notation to be consistent with the LocalFirehose syntax.
This commit contains the cleanup needed for the new integration test framework.
Changes:
- Fix log lines, misspellings, docs, etc.
- Allow the use of some of Druid's "JSON config" objects in tests
- Fix minor bug in `BaseNodeRoleWatcher`
The web-console (indirectly) calls the Overlord’s GET tasks API to fetch the tasks' summary which in turn queries the metadata tasks table. This query tries to fetch several columns, including payload, of all the rows at once. This introduces a significant memory overhead and can cause unresponsiveness or overlord failure when the ingestion tab is opened multiple times (due to several parallel calls to this API)
Another thing to note is that the task table (the payload column in particular) can be very large. Extracting large payloads from such tables can be very slow, leading to slow UI. While we are fixing the memory pressure in the overlord, we can also fix the slowness in UI caused by fetching large payloads from the table. Fetching large payloads also puts pressure on the metadata store as reported in the community (Metadata store query performance degrades as the tasks in druid_tasks table grows · Issue #12318 · apache/druid )
The task summaries returned as a response for the API are several times smaller and can fit comfortably in memory. So, there is an opportunity here to fix the memory usage, slow ingestion, and under-pressure metadata store by removing the need to handle large payloads in every layer we can. Of course, the solution becomes complex as we try to fix more layers. With that in mind, this page captures two approaches. They vary in complexity and also in the degree to which they fix the aforementioned problems.
* Ensure ByteBuffers allocated in tests get freed.
Many tests had problems where a direct ByteBuffer would be allocated
and then not freed. This is bad because it causes flaky tests.
To fix this:
1) Add ByteBufferUtils.allocateDirect(size), which returns a ResourceHolder.
This makes it easy to free the direct buffer. Currently, it's only used
in tests, because production code seems OK.
2) Update all usages of ByteBuffer.allocateDirect (off-heap) in tests either
to ByteBuffer.allocate (on-heap, which are garbaged collected), or to
ByteBufferUtils.allocateDirect (wherever it seemed like there was a good
reason for the buffer to be off-heap). Make sure to close all direct
holders when done.
* Changes based on CI results.
* A different approach.
* Roll back BitmapOperationTest stuff.
* Try additional surefire memory.
* Revert "Roll back BitmapOperationTest stuff."
This reverts commit 49f846d9e3.
* Add TestBufferPool.
* Revert Xmx change in tests.
* Better behaved NestedQueryPushDownTest. Exit tests on OOME.
* Fix TestBufferPool.
* Remove T1C from ARM tests.
* Somewhat safer.
* Fix tests.
* Fix style stuff.
* Additional debugging.
* Reset null / expr configs better.
* ExpressionLambdaAggregatorFactory thread-safety.
* Alter forkNode to try to get better info when a JVM crashes.
* Fix buffer retention in ExpressionLambdaAggregatorFactory.
* Remove unused import.
* GroupBy: Reduce allocations by reusing entry and key holders.
Two main changes:
1) Reuse Entry objects returned by various implementations of
Grouper.iterator.
2) Reuse key objects contained within those Entry objects.
This is allowed by the contract, which states that entries must be
processed and immediately discarded. However, not all call sites
respected this, so this patch also updates those call sites.
One particularly sneaky way that the old code retained entries too long
is due to Guava's MergingIterator and CombiningIterator. Internally,
these both advance to the next value prior to returning the current
value. So, this patch addresses that in two ways:
1) For merging, we have our own implementation MergeIterator already,
although it had the same problem. So, this patch updates our
implementation to return the current item prior to advancing to the
next item. It also adds a forbidden-api entry to ensure that this
safer implementation is used instead of Guava's.
2) For combining, we address the problem in a different way: by copying
the key when creating the new, combined entry.
* Attempt to fix test.
* Remove unused import.
The query context is a way that the user gives a hint to the Druid query engine, so that they enforce a certain behavior or at least let the query engine prefer a certain plan during query planning. Today, there are 3 types of query context params as below.
Default context params. They are set via druid.query.default.context in runtime properties. Any user context params can be default params.
User context params. They are set in the user query request. See https://druid.apache.org/docs/latest/querying/query-context.html for parameters.
System context params. They are set by the Druid query engine during query processing. These params override other context params.
Today, any context params are allowed to users. This can cause
1) a bad UX if the context param is not matured yet or
2) even query failure or system fault in the worst case if a sensitive param is abused, ex) maxSubqueryRows.
This PR adds an ability to limit context params per user role. That means, a query will fail if you have a context param set in the query that is not allowed to you. To do that, this PR adds a new built-in resource type, QUERY_CONTEXT. The resource to authorize has a name of the context param (such as maxSubqueryRows) and the type of QUERY_CONTEXT. To allow a certain context param for a user, the user should be granted WRITE permission on the context param resource. Here is an example of the permission.
{
"resourceAction" : {
"resource" : {
"name" : "maxSubqueryRows",
"type" : "QUERY_CONTEXT"
},
"action" : "WRITE"
},
"resourceNamePattern" : "maxSubqueryRows"
}
Each role can have multiple permissions for context params. Each permission should be set for different context params.
When a query is issued with a query context X, the query will fail if the user who issued the query does not have WRITE permission on the query context X. In this case,
HTTP endpoints will return 403 response code.
JDBC will throw ForbiddenException.
Note: there is a context param called brokerService that is used only by the router. This param is used to pin your query to run it in a specific broker. Because the authorization is done not in the router, but in the broker, if you have brokerService set in your query without a proper permission, your query will fail in the broker after routing is done. Technically, this is not right because the authorization is checked after the context param takes effect. However, this should not cause any user-facing issue and thus should be OK. The query will still fail if the user doesn’t have permission for brokerService.
The context param authorization can be enabled using druid.auth.authorizeQueryContextParams. This is disabled by default to avoid any hassle when someone upgrades his cluster blindly without reading release notes.
Currently while loading a lookup for the first time, loading threads blocks
for `waitForFirstRunMs` incase the lookup failed to load. If the `waitForFirstRunMs`
is long (like 10 minutes), such blocking can slow down the loading of other lookups.
This commit allows the thread to progress as soon as the loading of the lookup fails.
amazon-kinesis-client was not covered undered the apache license and required separate insertion in the kinesis extension.
This can now be avoided since it is covered, and including it within druid helps prevent incompatibilities.
Allows enabling of deaggregation out of the box by packaging amazon-kinesis-client (1.14.4) with druid for kinesis ingestion.
listShards API was used to get all the shards for kinesis ingestion to improve its resiliency as part of #12161.
However, this may require additional permissions in the IAM policy where the stream is present. (Please refer to: https://docs.aws.amazon.com/kinesis/latest/APIReference/API_ListShards.html).
A dynamic configuration useListShards has been added to KinesisSupervisorTuningConfig to control the usage of this API and prevent issues upon upgrade. It can be safely turned on (and is recommended when using kinesis ingestion) by setting this configuration to true.
* Store null columns in the segments
* fix test
* remove NullNumericColumn and unused dependency
* fix compile failure
* use guava instead of apache commons
* split new tests
* unused imports
* address comments
* kubernetes: restart watch on null response
Kubernetes watches allow a client to efficiently processes changes to
resources. However, they have some idiosyncrasies. In particular, they
can error out for various reasons leading to what would normally be seen
as an invalid result.
The Druid kubernetes node discovery subsystem does not handle a certain
case properly. The watch can return an item with a null object. These
leads to a null pointer exception. When this happens, the provider needs
to restart the watch, because rerunning the watch from the same resource
version leads to the same result: yet another null pointer exception.
This commit changes the provider to handle null objects by restarting
the watch.
* review: add more coverage
This adds a bit more coverage to the K8sDruidNodeDiscoveryProvider watch
loop, and removes an unnecessay return.
* kubernetes: reduce logging verbosity
The log messages about items being NULL don't really deserve to be at a
level other than DEBUG since they are not actionable, particularly since
we automatically recover now. Move them to the DEBUG level.
* Always reopen stream in FileUtils.copyLarge, RetryingInputStream.
When an InputStream throws an exception from one of its read methods,
we should assume it's bad and reopen it.
The main changes here are:
- In FileUtils.copyLarge, replace InputStream with InputStreamSupplier.
- In RetryingInputStream, collapse retryCondition and resetCondition
into a single condition. Also, make it required, since every usage
is passing in a specific condition anyway.
* Test fixes.
* Fix read impl.
These changes are to use the latest datasketches-java-3.1.0 and also to restore support for quantile and HLL4 sketches to be able to grow larger than a given buffer in a buffer aggregator and move to heap in rare cases. This was discussed in #11544.
Co-authored-by: AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com>
This PR aims to make the ParseExceptions in Druid more informative, by adding additional information (metadata) to the ParseException, which can contain additional information about the exception. For example - the path of the file generating the issue, the line number (where it can be easily fetched - like CsvReader)
Following changes are addressed in this PR:
A new class CloseableIteratorWithMetadata has been created which is like CloseableIterator but also has a metadata method that returns a context Map<String, Object> about the current element returned by next().
IntermediateRowParsingReader#read() now attaches the InputEntity and the "record number" which created the exception (while parsing them), and IntermediateRowParsingReader#sample attaches the InputEntity (but not the "record number").
TextReader (and its subclasses), which is a specific implementation of the IntermediateRowParsingReader also include the line number which caused the generation of the error.
This will also help in triaging the issues when InputSourceReader generates ParseException because it can point to the specific InputEntity which caused the exception (while trying to read it).
Mockito now supports all our needs and plays much better with recent Java versions.
Migrating to Mockito also simplifies running the kind of tests that required PowerMock in the past.
* replace all uses of powermock with mockito-inline
* upgrade mockito to 4.3.1 and fix use of deprecated methods
* import mockito bom to align all our mockito dependencies
* add powermock to forbidden-apis to avoid accidentally reintroducing it in the future
* remove use of mocks for ServiceMetricEvent
* simplify KafkaEmitterTests by moving to Mockito
* speed up KafkaEmitterTest by adjusting reporting frequency in tests
* remove unnecessary easymock and JUnitParams dependencies
Azure Blob storage has multiple modes of authentication. One of them is Shared access resource
. This is very useful in cases when we do not want to add the account key in the druid properties .
Problem:
- When a kinesis stream is resharded, the original shards are closed.
Any intermediate shard created in the process is eventually closed as well.
- If a shard is closed before any record is put into it, it can be safely ignored for ingestion.
- It is expensive to determine if a closed shard is empty, since it requires a call to the Kinesis cluster.
Changes:
- Maintain a cache of closed empty and closed non-empty shards in `KinesisSupervisor`
- Add config `skipIngorableShards` to `KinesisSupervisorTuningConfig`
- The caches are used and updated only when `skipIgnorableShards = true`
* rework sql planner expression and virtual column handling
* simplify a bit
* add back and deprecate old methods, more tests, fix multi-value string coercion bug and associated tests
* spotbugs
* fix bugs with multi-value string array expression handling
* javadocs and adjust test
* better
* fix tests
* working
* Lazily load segmentKillers, segmentMovers, and segmentArchivers
* more tests
* test-jar plugin
* more coverage
* lazy client
* clean up changes
* checkstyle
* i did not change the branch condition
* adjust failure rate to run tests faster
* javadocs
* checkstyle
* Harmonize implementations of "visit" for Exprs from ExprMacros.
Many of them had bugs where they would not visit all of the original
arguments. I don't think this has user-visible consequences right now,
but it's possible it would in a future world where "visit" is used
for more stuff than it is today.
So, this patch all updates all implementations to a more consistent
style that emphasizes reapplying the macro to the shuttled args.
* Test fixes, test coverage, PR review comments.
Fixes#12022
### Description
The current implementations of memory estimation in `OnHeapIncrementalIndex` and `StringDimensionIndexer` tend to over-estimate which leads to more persistence cycles than necessary.
This PR replaces the max estimation mechanism with getting the incremental memory used by the aggregator or indexer at each invocation of `aggregate` or `encode` respectively.
### Changes
- Add new flag `useMaxMemoryEstimates` in the task context. This overrides the same flag in DefaultTaskConfig i.e. `druid.indexer.task.default.context` map
- Add method `AggregatorFactory.factorizeWithSize()` that returns an `AggregatorAndSize` which contains
the aggregator instance and the estimated initial size of the aggregator
- Add method `Aggregator.aggregateWithSize()` which returns the incremental memory used by this aggregation step
- Update the method `DimensionIndexer.processRowValsToKeyComponent()` to return the encoded key component as well as its effective size in bytes
- Update `OnHeapIncrementalIndex` to use the new estimations only if `useMaxMemoryEstimates = false`
Follow up to #12205 to allow druid-mysql-extensions to work with mysql connector/j 8.x again, which does not contain MySQLTransientException, and while would have had the same problem as mariadb if a transient exception was checked, the new check eagerly loads the class when starting up, causing immediate failure.
Makes kinesis ingestion resilient to `LimitExceededException` caused by resharding.
Replace `describeStream` with `listShards` (recommended) to get shard related info.
`describeStream` has a limit (100) to the number of shards returned per call and a low default TPS limit of 10.
`listShards` returns the info for at most 1000 shards and has a higher TPS limit of 100 as well.
Key changed/added classes in this PR
* `KinesisRecordSupplier`
* `KinesisAdminClient`
This fixes a bug that causes TaskClient in overlord to continuously retry to pause tasks. This can happen when a task is not responding to the pause command. Ideally, in such a case when the task is unresponsive, the overlord would have given up after a few retries and would have killed the task. However, due to this bug, retries go on forever.
* Ingestion will fail for HLLSketchBuild instead of creating with incorrect values
* Addressing review comments for HLL< updated error message introduced test case
* Add jsonPath functions support
* Add jsonPath function test for Avro
* Add jsonPath function length() to Orc
* Add jsonPath function length() to Parquet
* Add more tests to ORC format
* update doc
* Fix exception during ingestion
* Add IT test case
* Revert "Fix exception during ingestion"
This reverts commit 5a5484b9ea.
* update IT test case
* Add 'keys()'
* Commit IT test case
* Fix UT
This PR fixes an issue in which if a lookup is configured incorreclty; does not serialize properly when being pulled by peon node, it causes the task to fail. The failure occurs because the peon and other leaf nodes (broker, historical), have retry logic that continues to retry the lookup loading for 3 minutes by default. The http listener thread on the peon task is not started until lookup loading completes, by default, the overlord waits 1 minute by default, to communicate with the peon task to get the task status, after which is orders the task to shut down, causing the ingestion task to fail.
To fix the issue, we catch the exception serialization error, and do not retry. Also fixed an issue in which a bad lookup config interferes with any other good lookup configs from being loaded.
* Enhancements to IndexTaskClient.
1) Ability to use handlers other than StringFullResponseHandler. This
functionality is not used in production code yet, but is useful
because it will allow tasks to communicate with each other in
non-string-based formats and in streaming fashion. In the future,
we'll be able to use this to make task-to-task communication
more efficient.
2) Truncate server errors at 1KB, so long errors do not pollute logs.
3) Change error log level for retryable errors from WARN to INFO. (The
final error is still WARN.)
4) Harmonize log and exception messages to have a more consistent format.
* Additional tests and improvements.
This PR fixes a problem where the com.sun.jndi.ldap.Connection tries to build BasicSecuritySSLSocketFactory when calling LDAPCredentialsValidator.validateCredentials since BasicSecuritySSLSocketFactory is in extension class loader and not visible to system classloader.
changes:
* adds new config, druid.expressions.useStrictBooleans which make longs the official boolean type of all expressions
* vectorize logical operators and boolean functions, some only if useStrictBooleans is true
* Code cleanup from query profile project
* Fix spelling errors
* Fix Javadoc formatting
* Abstract out repeated test code
* Reuse constants in place of some string literals
* Fix up some parameterized types
* Reduce warnings reported by Eclipse
* Reverted change due to lack of tests
Add a "guessAggregatorHeapFootprint" method to AggregatorFactory that
mitigates #6743 by enabling heap footprint estimates based on a specific
number of rows. The idea is that at ingestion time, the number of rows
that go into an aggregator will be 1 (if rollup is off) or will likely
be a small number (if rollup is on).
It's a heuristic, because of course nothing guarantees that the rollup
ratio is a small number. But it's a common case, and I expect this logic
to go wrong much less often than the current logic. Also, when it does
go wrong, users can fix it by lowering maxRowsInMemory or
maxBytesInMemory. The current situation is unintuitive: when the
estimation goes wrong, users get an OOME, but actually they need to
*raise* these limits to fix it.
* Add support for custom reset condition & support for other args to have defaults to make the method api consistent
* Add support for custom reset condition to InputEntity
* Fix test names
* Clarifying comments to why we need to read the message's content to identify S3's resettable exception
* Add unit test to verify custom resettable condition for S3Entity
* Provide a way to customize retries since they are expensive to test
* add back and deprecate aggregator factory methods so i can say i told you so when i delete these later
* rename to make less ambiguous, fix fill method
* adjust
* add missing json type for ListFilteredVirtualColumn, and tests to try to avoid this happening again
* fixes
* ugly, but maybe this
* oops
* too many mappers
* complex typed expressions
* add built-in hll collector expressions to get coverage on druid-processing, more types, more better
* rampage!!!
* more javadoc
* adjustments
* oops
* lol
* remove unused dependency
* contradiction?
* more test
Enhanced the ExtractionNamespace interface in lookups-cached-global core extension with the ability to set a maxHeapPercentage for the cache of the respective namespace. The reason for adding this functionality, is make it easier to detect when a lookup table grows to a size that the underlying service cannot handle, because it does not have enough memory. The default value of maxHeap for the interface is -1, which indicates that no maxHeapPercentage has been set. For the JdbcExtractionNamespace and UriExtractionNamespace implementations, the default value is null, which will cause the respective service that the lookup is loaded in, to warn when its cache is beyond mxHeapPercentage of the service's configured max heap size. If a positive non-null value is set for the namespace's maxHeapPercentage config, this value will be honored for all services that the respective lookup is loaded onto, and consequently log warning messages when the cache of the respective lookup grows beyond this respective percentage of the services configured max heap size. Warnings are logged every time that either Uri based or Jdbc based lookups are regenerated, if the maxHeapPercentage constraint is violated. No other implementations will log warnings at this time. No error is thrown when the size exceeds the maxHeapPercentage at this time, as doing so could break functionality for existing users. Previously the JdbcCacheGenerator generated its cache by materializing all rows of the underling table in memory at once; this made it difficult to log warning messages in the case that the results from the jdbc query were very large and caused the service to run out of memory. To help with this, this pr makes it so that the jdbc query results are instead streamed through an iterator.
Add support for hadoop 3 profiles . Most of the details are captured in #11791 .
We use a combination of maven profiles and resource filtering to achieve this. Hadoop2 is supported by default and a new maven profile with the name hadoop3 is created. This will allow the user to choose the profile which is best suited for the use case.
* Remove OffheapIncrementalIndex and clarify aggregator thread-safety needs.
This patch does the following:
- Removes OffheapIncrementalIndex.
- Clarifies that Aggregators are required to be thread safe.
- Clarifies that BufferAggregators and VectorAggregators are not
required to be thread safe.
- Removes thread safety code from some DataSketches aggregators that
had it. (Not all of them did, and that's OK, because it wasn't necessary
anyway.)
- Makes enabling "useOffheap" with groupBy v1 an error.
Rationale for removing the offheap incremental index:
- It is only used in one rare scenario: groupBy v1 (which is non-default)
in "useOffheap" mode (also non-default). So you have to go pretty deep
into the wilderness to get this code to activate in production. It is
never used during ingestion.
- Its existence complicates developer efforts to reason about how
aggregators get used, because the way it uses buffer aggregators is so
different from how every other query engine uses them.
- It doesn't have meaningful testing.
By the way, I do believe that the given way the offheap incremental index
works, it actually didn't require buffer aggregators to be thread-safe.
It synchronizes on "aggregate" and doesn't call "get" until it has
stopped calling "aggregate". Nevertheless, this is a bother to think about,
and for the above reasons I think it makes sense to remove the code anyway.
* Remove things that are now unused.
* Revert removal of getFloat, getLong, getDouble from BufferAggregator.
* OAK-related warnings, suppressions.
* Unused item suppressions.
* Add druid.sql.approxCountDistinct.function property.
The new property allows admins to configure the implementation for
APPROX_COUNT_DISTINCT and COUNT(DISTINCT expr) in approximate mode.
The motivation for adding this setting is to enable site admins to
switch the default HLL implementation to DataSketches.
For example, an admin can set:
druid.sql.approxCountDistinct.function = APPROX_COUNT_DISTINCT_DS_HLL
* Fixes
* Fix tests.
* Remove erroneous cannotVectorize.
* Remove unused import.
* Remove unused test imports.
* SQL: Allow Scans to be used as outer queries.
This has been possible in the native query system for a while, but the capability
hasn't yet propagated into the SQL layer. One example of where this is useful is
a query like:
SELECT * FROM (... LIMIT X) WHERE <filter>
Because this expands the kinds of subquery structures the SQL layer will consider,
it was also necessary to improve the cost calculations. These changes appear in
PartialDruidQuery and DruidOuterQueryRel. The ideas are:
- Attach per-column penalties to the output signature of each query, instead of to
the initial projection that starts a query. This encourages moving projections
into subqueries instead of leaving them on outer queries.
- Only attach penalties to projections if there are actually expressions happening.
So, now, projections that simply reorder or remove fields are free.
- Attach a constant penalty to every outer query. This discourages creating them
when they are not needed.
The changes are generally beneficial to the test cases we have in CalciteQueryTest.
Most plans are unchanged, or are changed in purely cosmetic ways. Two have changed
for the better:
- testUsingSubqueryWithLimit now returns a constant from the subquery, instead of
returning every column.
- testJoinOuterGroupByAndSubqueryHasLimit returns a minimal set of columns from
the innermost subquery; two unnecessary columns are no longer there.
* Fix various DS operator conversions.
These were all implemented as direct conversions, which isn't appropriate
because they do not actually map onto native functions. These are only
usable as post-aggregations.
* Test case adjustment.
* Remove CloseQuietly and migrate its usages to other methods.
These other methods include:
1) New method CloseableUtils.closeAndWrapExceptions, which wraps IOExceptions
in RuntimeExceptions for callers that just want to avoid dealing with
checked exceptions. Most usages were migrated to this method, because it
looks like they were mainly attempts to avoid declaring a throws clause,
and perhaps were unintentionally suppressing IOExceptions.
2) New method CloseableUtils.closeInCatch, designed to properly close something
in a catch block without losing exceptions. Some usages from catch blocks
were migrated here, when it seemed that they were intended to avoid checked
exception handling, and did not really intend to also suppress IOExceptions.
3) New method CloseableUtils.closeAndSuppressExceptions, which sends all
exceptions to a "chomper" that consumes them. Nothing is thrown or returned.
The behavior is slightly different: with this method, _all_ exceptions are
suppressed, not just IOExceptions. Calls that seemed like they had good
reason to suppress exceptions were migrated here.
4) Some calls were migrated to try-with-resources, in cases where it appeared
that CloseQuietly was being used to avoid throwing an exception in a finally
block.
🎵 You don't have to go home, but you can't stay here... 🎵
* Remove unused import.
* Fix up various issues.
* Adjustments to tests.
* Fix null handling.
* Additional test.
* Adjustments from review.
* Fixup style stuff.
* Fix NPE caused by holder starting out null.
* Fix spelling.
* Chomp Throwables too.
* Null handling fixes for DS HLL and Theta sketches.
For HLL, this fixes an NPE when processing a null in a multi-value dimension.
For both, empty strings are now properly treated as nulls (and ignored) in
replace-with-default mode. Behavior in SQL-compatible mode is unchanged.
* Fix expectation.
* add ColumnInspector argument to PostAggregator.getType to allow post-aggs to compute their output type based on input types
* add test for test for coverage
* simplify
* Remove unused imports.
Co-authored-by: Gian Merlino <gian@imply.io>
* latest datasketches-java and datasketches-memory
* updated versions of datasketches-java and datasketches-memory
Co-authored-by: AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com>
* better type system
* needle in a haystack
* ColumnCapabilities is a TypeSignature instead of having one, INFORMATION_SCHEMA support
* fixup merge
* more test
* fixup
* intern
* fix
* oops
* oops again
* ...
* more test coverage
* fix error message
* adjust interning, more javadocs
* oops
* more docs more better
### Description
Today we ingest a number of high cardinality metrics into Druid across dimensions. These metrics are rolled up on a per minute basis, and are very useful when looking at metrics on a partition or client basis. Events is another class of data that provides useful information about a particular incident/scenario inside a Kafka cluster. Events themselves are carried inside kafka payload, but nonetheless there are some very useful metadata that is carried in kafka headers that can serve as useful dimension for aggregation and in turn bringing better insights.
PR(https://github.com/apache/druid/pull/10730) introduced support of Kafka headers in InputFormats.
We still need an input format to parse out the headers and translate those into relevant columns in Druid. Until that’s implemented, none of the information available in the Kafka message headers would be exposed. So first there is a need to write an input format that can parse headers in any given format(provided we support the format) like we parse payloads today. Apart from headers there is also some useful information present in the key portion of the kafka record. We also need a way to expose the data present in the key as druid columns. We need a generic way to express at configuration time what attributes from headers, key and payload need to be ingested into druid. We need to keep the design generic enough so that users can specify different parsers for headers, key and payload.
This PR is designed to solve the above by providing wrapper around any existing input formats and merging the data into a single unified Druid row.
Lets look at a sample input format from the above discussion
"inputFormat":
{
"type": "kafka", // New input format type
"headerLabelPrefix": "kafka.header.", // Label prefix for header columns, this will avoid collusions while merging columns
"recordTimestampLabelPrefix": "kafka.", // Kafka record's timestamp is made available in case payload does not carry timestamp
"headerFormat": // Header parser specifying that values are of type string
{
"type": "string"
},
"valueFormat": // Value parser from json parsing
{
"type": "json",
"flattenSpec": {
"useFieldDiscovery": true,
"fields": [...]
}
},
"keyFormat": // Key parser also from json parsing
{
"type": "json"
}
}
Since we have independent sections for header, key and payload, it will enable parsing each section with its own parser, eg., headers coming in as string and payload as json.
KafkaInputFormat will be the uber class extending inputFormat interface and will be responsible for creating individual parsers for header, key and payload, blend the data resolving conflicts in columns and generating a single unified InputRow for Druid ingestion.
"headerFormat" will allow users to plug parser type for the header values and will add default header prefix as "kafka.header."(can be overridden) for attributes to avoid collision while merging attributes with payload.
Kafka payload parser will be responsible for parsing the Value portion of the Kafka record. This is where most of the data will come from and we should be able to plugin existing parser. One thing to note here is that if batching is performed, then the code is augmenting header and key values to every record in the batch.
Kafka key parser will handle parsing Key portion of the Kafka record and will ingest the Key with dimension name as "kafka.key".
## KafkaInputFormat Class:
This is the class that orchestrates sending the consumerRecord to each parser, retrieve rows, merge the columns into one final row for Druid consumption. KafkaInputformat should make sure to release the resources that gets allocated as a part of reader in CloseableIterator<InputRow> during normal and exception cases.
During conflicts in dimension/metrics names, the code will prefer dimension names from payload and ignore the dimension either from headers/key. This is done so that existing input formats can be easily migrated to this new format without worrying about losing information.
* refactor sql authorization to get resource type from schema, refactor resource type from enum to string
* information schema auth filtering adjustments
* refactor
* minor stuff
* Update SqlResourceCollectorShuttle.java
When CommonCachedNotifier is being stopped while the thread is waiting on updateQueue.take(),
an InterruptedException is thrown. The stack trace from this exception gives the wrong idea that something went wrong with the shutdown.
* Make persists concurrent with ingestion
* Remove semaphore but keep concurrent persists (with add) and add push in the backround as well
* Go back to documented default persists (zero)
* Move to debug
* Remove unnecessary Atomics
* Comments on synchronization (or not) for sinks & sinkMetadata
* Some cleanup for unit tests but they still need further work
* Shutdown & wait for persists and push on close
* Provide support for three existing batch appenderators using batchProcessingMode flag
* Fix reference to wrong appenderator
* Fix doc typos
* Add BatchAppenderators class test coverage
* Add log message to batchProcessingMode final value, fix typo in enum name
* Another typo and minor fix to log message
* LEGACY->OPEN_SEGMENTS, Edit docs
* Minor update legacy->open segments log message
* More code comments, mostly small adjustments to naming etc
* fix spelling
* Exclude BtachAppenderators from Jacoco since it is fully tested but Jacoco still refuses to ack coverage
* Coverage for Appenderators & BatchAppenderators, name change of a method that was still using "legacy" rather than "openSegments"
Co-authored-by: Clint Wylie <cjwylie@gmail.com>
* Configurable maxStreamLength for doubles sketches
* fix equals/hashcode and it test failure
* fix test
* fix it test
* benchmark
* doc
* grouping key
* fix comment
* dependency check
* Update docs/development/extensions-core/datasketches-quantiles.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Fixes#11297.
Description
Description and design in the proposal #11297
Key changed/added classes in this PR
*DataSegmentPusher
*ShuffleClient
*PartitionStat
*PartitionLocation
*IntermediaryDataManager
This PR adds a new property druid.router.sql.enable which allows the
Router to handle SQL queries when set to true.
This change does not affect Avatica JDBC requests and they are still routed
by hashing the Connection ID.
To allow parsing of the request object as a SqlQuery (contained in module druid-sql),
some classes have been moved from druid-server to druid-services with
the same package name.
* Better logging for lookups
The default pollPeriod of 0 means that lookups are loaded once only at startup
Add a warning message to warn operators about this. I suspect that most
operators using jdbc or uri would expect eventual consistency with the source
of the lookups if using jdbc or uri. So make this a warning to make it easier
to debug if an operator notices a data inconsistency issue.
* oops
* Add error msg to parallel task's TaskStatus
* Consolidate failure block
* Add failure test
* Make it fail
* Add fail while stopped
* Simplify hash task test using a runner that fails after so many runs (parameter)
* Remove unthrown exception
* Use runner names to identify phase
* Added range partition kill test & fixed a timing bug with the custom runner
* Forbidden api
* Style
* Unit test code cleanup
* Added message to invalid state exception and improved readability of the phase error messages for the parallel task failure unit tests
* Add back missing unit test coverage in AvroFlattenerMakerTest
Adds back test coverage for Avro flattener that was mistakenly removed in https://github.com/apache/druid/pull/10505. Recfactored the tests a bit too.
* resolve checkstyle warnings
This PR splits current SegmentLoader into SegmentLoader and SegmentCacheManager.
SegmentLoader - this class is responsible for building the segment object but does not expose any methods for downloading, cache space management, etc. Default implementation delegates the download operations to SegmentCacheManager and only contains the logic for building segments once downloaded. . This class will be used in SegmentManager to construct Segment objects.
SegmentCacheManager - this class manages the segment cache on the local disk. It fetches the segment files to the local disk, can clean up the cache, and in the future, support reserve and release on cache space. [See https://github.com/Make SegmentLoader extensible and customizable #11398]. This class will be used in ingestion tasks such as compaction, re-indexing where segment files need to be downloaded locally.
Karan Kumar
Gian Merlino
imply-cheddar
zachjsh
Clint Wylie
Abhishek Radhakrishnan
Paul Rogers
Laksh Singla
frankgrimes97
Rishabh Singh
Atul Mohan
Adarsh Sanjeev
Tejaswini Bandlamudi
Rohan Garg
Nicholas Lippis
Anshu Makkar
Abhishek Agarwal
somu-imply
Maytas Monsereenusorn
Kashif Faraz
AmatyaAvadhanula
xiaokang
Didip Kerabat
Dr. Sizzles
Jason Koch
hnakamor
Jonathan Wei
Frank Chen
Ellen Shen
Alexander Saydakov
Junge
William Hyun
David Palmer
Hamish Ball
Rui Chen
Dongjoon Hyun
Lucas Capistrant
MC-JY
Gian Merlino
Jihoon Son
PJ Fanning
AmatyaAvadhanula
Kyle Larose
Parag Jain
Laksh Singla
Xavier Léauté
Suneet Saldanha
Agustin Gonzalez
lokesh-lingarajan
zhangyue19921010
dependabot[bot]
Yi Yuan