* 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
* fix array_agg to work with complex types and bugs with expression aggregator complex array handling
* more consistent handling of array expressions, numeric arrays more consistently honor druid.generic.useDefaultValueForNull, fix array_ordinal sql output type
### Description
This change adds a new config property `druid.sql.planner.operatorConversion.denyList`, which allows a user to specify
any operator conversions that they wish to disallow. A user may want to do this for a number of reasons, including security concerns. The default value of this property is the empty list `[]`, which does not disallow any operator conversions.
An example usage of this property is `druid.sql.planner.operatorConversion.denyList=["extern"]`, which disallows the usage of the `extern` operator conversion. If the property is configured this way, and a user of the Druid cluster tries to submit a query that uses the `extern` function, such as the example given [here](https://druid.apache.org/docs/latest/multi-stage-query/examples.html#insert-with-no-rollup), a response with http response code `400` is returned with en error body similar to the following:
```
{
"taskId": "4ec5b0b6-fa9b-4c3a-827d-2308294e9985",
"state": "FAILED",
"error": {
"error": "Plan validation failed",
"errorMessage": "org.apache.calcite.runtime.CalciteContextException: From line 28, column 5 to line 32, column 5: No match found for function signature EXTERN(<CHARACTER>, <CHARACTER>, <CHARACTER>)",
"errorClass": "org.apache.calcite.tools.ValidationException",
"host": null
}
}
```
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
* Adjust Operators to be Pausable
This enables "merge" style operations that
combine multiple streams.
This change includes a naive implementation
of one such merge operator just to provide
concrete evidence that the refactoring is
effective.
* adds the SQL component of the native unnest functionality in Druid to unnest SQL queries on a table dimension, virtual column or a constant array and convert them into native Druid queries
* unnest in SQL is implemented as a combination of Correlate (the comma join part) and Uncollect (the unnest part)
* 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>
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
* single typed "root" only nested columns now mimic "regular" columns of those types
* incremental index can now use nested column indexer instead of string indexer for discovered columns
* 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
* Unify the handling of HTTP between SQL and Native
The SqlResource and QueryResource have been
using independent logic for things like error
handling and response context stuff. This
became abundantly clear and painful during a
change I was making for Window Functions, so
I unified them into using the same code for
walking the response and serializing it.
Things are still not perfectly unified (it would
be the absolute best if the SqlResource just
took SQL, planned it and then delegated the
query run entirely to the QueryResource), but
this refactor doesn't take that fully on.
The new code leverages async query processing
from our jetty container, the different
interaction model with the Resource means that
a lot of tests had to be adjusted to align with
the async query model. The semantics of the
tests remain the same with one exception: the
SqlResource used to not log requests that failed
authorization checks, now it does.
* bump nested column format version
changes:
* nested field files are now named by their position in field paths list, rather than directly by the path itself. this fixes issues with valid json properties with commas and newlines breaking the csv file meta.smoosh
* update StructuredDataProcessor to deal in NestedPathPart to be consistent with other abstract path handling rather than building JQ syntax strings directly
* add v3 format segment and test
* Support Framing for Window Aggregations
This adds support for framing over ROWS
for window aggregations.
Still not implemented as yet:
1. RANGE frames
2. Multiple different frames in the same query
3. Frames on last/first functions
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.
* Processors for Window Processing
This is an initial take on how to use Processors
for Window Processing. A Processor is an interface
that transforms RowsAndColumns objects.
RowsAndColumns objects are essentially combinations
of rows and columns.
The intention is that these Processors are the start
of a set of operators that more closely resemble what
DB engineers would be accustomed to seeing.
* Wire up windowed processors with a query type that
can run them end-to-end. This code can be used to
actually run a query, so yay!
* Wire up windowed processors with a query type that
can run them end-to-end. This code can be used to
actually run a query, so yay!
* Some SQL tests for window functions. Added wikipedia
data to the indexes available to the
SQL queries and tests validating the windowing
functionality as it exists now.
Co-authored-by: Gian Merlino <gianmerlino@gmail.com>
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
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
* 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.
* 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
* Fix two sources of SQL statement leaks.
1) SqlTaskResource and DruidJdbcResultSet leaked statements 100% of the
time, since they call stmt.plan(), which adds statements to
SqlLifecycleManager, and they do not explicitly remove them.
2) SqlResource leaked statements if yielder.close() threw an exception.
(And also would not emit metrics, since in that case it failed to
call stmt.close as well.)
* Only closeQuietly is needed.
* 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.
Async reads for JDBC:
Prevents JDBC timeouts on long queries by returning empty batches
when a batch fetch takes too long. Uses an async model to run the
result fetch concurrently with JDBC requests.
Fixed race condition in Druid's Avatica server-side handler
Fixed issue with no-user connections
* 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
* fix json_value sql planning with decimal type, fix vectorized expression math null value handling in default mode
changes:
* json_value 'returning' decimal will now plan to native double typed query instead of ending up with default string typing, allowing decimal vector math expressions to work with this type
* vector math expressions now zero out 'null' values even in 'default' mode (druid.generic.useDefaultValueForNull=false) to prevent downstream things that do not check the null vector from producing incorrect results
* more better
* test and why not vectorize
* more test, more fix
This adds a sql function, "BIG_SUM", that uses
CompressedBigDecimal to do a sum. Other misc changes:
1. handle NumberFormatExceptions when parsing a string (default to set
to 0, configurable in agg factory to be strict and throw on error)
2. format pom file (whitespace) + add dependency
3. scaleUp -> scale and always require scale as a parameter
* 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
* SQL: Fix round-trips of floating point literals.
When writing RexLiterals into Druid expressions, we now write non-integer
numeric literals in such a way that ensures they are parsed as doubles
on the other end.
* Updates from code review, and some additional stuff inspired by the
investigation.
- Remove unnecessary formatting code from DruidExpression.doubleLiteral:
it handles things just fine with its default behavior.
- Fix a problem where expression literals could not represent Long.MIN_VALUE.
Now, integer literals start life off as BigIntegerExpr instead of LongExpr,
and are converted to LongExpr during flattening. This is necessary because,
in order to avoid ambiguity between unary minus and negative literals, our
grammar does not actually have true negative literals. Negative numbers must
be represented as unary minus next to a positive literal.
- Fix a bug introduced in #12230 where shuttle.visitAll(args) delegated
to shuttle.visit(arg) instead of arg.visit(shuttle). The latter does
a recursive visitation, which is the intended behavior.
* Style fixes.
* Move regexp to the right place.
* 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.
* 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
Two changes:
1) Restore the text of the SQL query. It was removed in #12897, but
then it was later pointed out that the text is helpful for end
users querying Druid through tools that do not show the SQL queries
that they are making.
2) Adjust wording slightly, from "Cannot build plan for query" to
"Query not supported". This will be clearer to most users. Generally
the reason we get these errors is due to unsupported SQL constructs.
* json_value adjustments
changes:
* native json_value expression now has optional 3rd argument to specify type, which will cast all values to the specified type
* rework how JSON_VALUE is wired up in SQL. Now we are using a custom convertlet to translate JSON_VALUE(... RETURNING type) into dedicated JSON_VALUE_BIGINT, JSON_VALUE_DOUBLE, JSON_VALUE_VARCHAR, JSON_VALUE_ANY instead of using the calcite StandardConvertletTable that wraps JSON_VALUE_ANY in a CAST, so that we preserve the typing of JSON_VALUE to pass down to the native expression as the 3rd argument
* fix json_value_any to be usable by humans too, coverage
* fix bug
* checkstyle
* checkstyle
* review stuff
* validate that options to json_value are the supported options rather than ignore them
* remove more legacy undocumented functions
The method wasn't following its contract, leading to pollution of the
overall planner context, when really we just want to create a new
context for a specific query.
* SQL: Morph QueryMakerFactory into SqlEngine.
Groundwork for introducing an indexing-service-task-based SQL engine
under the umbrella of #12262. Also includes some other changes related
to improving error behavior.
Main changes:
1) Elevate the QueryMakerFactory interface (an extension point that allows
customization of how queries are made) into SqlEngine. SQL engines
can influence planner behavior through EngineFeatures, and can fully
control the mechanics of query execution using QueryMakers.
2) Remove the server-wide QueryMakerFactory choice, in favor of the choice
being made by the SQL entrypoint. The indexing-service-task-based
SQL engine would be associated with its own entrypoint, like
/druid/v2/sql/task.
Other changes:
1) Adjust DruidPlanner to try either DRUID or BINDABLE convention based
on analysis of the planned rels; never try both. In particular, we
no longer try BINDABLE when DRUID fails. This simplifies the logic
and improves error messages.
2) Adjust error message "Cannot build plan for query" to omit the SQL
query text. Useful because the text can be quite long, which makes it
easy to miss the text about the problem.
3) Add a feature to block context parameters used internally by the SQL
planner from being supplied by end users.
4) Add a feature to enable adding row signature to the context for
Scan queries. This is useful in building the task-based engine.
5) Add saffron.properties file that turns off sets and graphviz dumps
in "cannot plan" errors. Significantly reduces log spam on the Broker.
* Fixes from CI.
* Changes from review.
* Can vectorize, now that join-to-filter is on by default.
* Checkstyle! And variable renames!
* Remove throws from test.
* 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
This is used to control access to the EXTERN function, which allows
reading external data in SQL. The EXTERN function is not usable in
production as of today, but it is used by the task-based SQL engine
contemplated in #12262.
Refactors the DruidSchema and DruidTable abstractions to prepare for the Druid Catalog.
As we add the catalog, we’ll want to combine physical segment metadata information with “hints” provided by the catalog. This is best done if we tidy up the existing code to more clearly separate responsibilities.
This PR is purely a refactoring move: no functionality changed. There is no difference to user functionality or external APIs. Functionality changes will come later as we add the catalog itself.
DruidSchema
In the present code, DruidSchema does three tasks:
Holds the segment metadata cache
Interfaces with an external schema manager
Acts as a schema to Calcite
This PR splits those responsibilities.
DruidSchema holds the Calcite schema for the druid namespace, combining information fro the segment metadata cache, from the external schema manager and (later) from the catalog.
SegmentMetadataCache holds the segment metadata cache formerly in DruidSchema.
DruidTable
The present DruidTable class is a bit of a kitchen sink: it holds all the various kinds of tables which Druid supports, and uses if-statements to handle behavior that differs between types. Yet, any given DruidTable will handle only one such table type. To more clearly model the actual table types, we split DruidTable into several classes:
DruidTable becomes an abstract base class to hold Druid-specific methods.
DatasourceTable represents a datasource.
ExternalTable represents an external table, such as from EXTERN or (later) from the catalog.
InlineTable represents the internal case in which we attach data directly to a table.
LookupTable represents Druid’s lookup table mechanism.
The new subclasses are more focused: they can be selective about the data they hold and the various predicates since they represent just one table type. This will be important as the catalog information will differ depending on table type and the new structure makes adding that logic cleaner.
DatasourceMetadata
Previously, the DruidSchema segment cache would work with DruidTable objects. With the catalog, we need a layer between the segment metadata and the table as presented to Calcite. To fix this, the new SegmentMetadataCache class uses a new DatasourceMetadata class as its cache entry to hold only the “physical” segment metadata information: it is up to the DruidTable to combine this with the catalog information in a later PR.
More Efficient Table Resolution
Calcite provides a convenient base class for schema objects: AbstractSchema. However, this class is a bit too convenient: all we have to do is provide a map of tables and Calcite does the rest. This means that, to resolve any single datasource, say, foo, we need to cache segment metadata, external schema information, and catalog information for all tables. Just so Calcite can do a map lookup.
There is nothing special about AbstractSchema. We can handle table lookups ourselves. The new AbstractTableSchema does this. In fact, all the rest of Calcite wants is to resolve individual tables by name, and, for commands we don’t use, to provide a list of table names.
DruidSchema now extends AbstractTableSchema. SegmentMetadataCache resolves individual tables (and provides table names.)
DruidSchemaManager
DruidSchemaManager provides a way to specify table schemas externally. In this sense, it is similar to the catalog, but only for datasources. It originally followed the AbstractSchema pattern: it implements provide a map of tables. This PR provides new optional methods for the table lookup and table names operations. The default implementations work the same way that AbstractSchema works: we get the entire map and pick out the information we need. Extensions that use this API should be revised to support the individual operations instead. Druid code no longer calls the original getTables() method.
The PR has one breaking change: since the DruidSchemaManager map is read-only to the rest of Druid, we should return a Map, not a ConcurrentMap.
* Adjust "in" filter null behavior to match "selector".
Now, both of them match numeric nulls if constructed with a "null" value.
This is consistent as far as native execution goes, but doesn't match
the behavior of SQL = and IN. So, to address that, this patch also
updates the docs to clarify that the native filters do match nulls.
This patch also updates the SQL docs to describe how Boolean logic is
handled in addition to how NULL values are handled.
Fixes#12856.
* Fix test.
* 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
add NumericRangeIndex interface and BoundFilter support
changes:
* NumericRangeIndex interface, like LexicographicalRangeIndex but for numbers
* BoundFilter now uses NumericRangeIndex if comparator is numeric and there is no extractionFn
* NestedFieldLiteralColumnIndexSupplier.java now supports supplying NumericRangeIndex for single typed numeric nested literal columns
* better faster stronger and (ever so slightly) more understandable
* more tests, fix bug
* fix style
* Druid planner now makes only one pass through Calcite planner
Resolves the issue that required two parse/plan cycles: one
for validate, another for plan. Creates a clone of the Calcite
planner and validator to resolve the conflict that prevented
the merger.
* Fixes for the Avatica JDBC driver
Correctly implement regular and prepared statements
Correctly implement result sets
Fix race condition with contexts
Clarify when parameters are used
Prepare for single-pass through the planner
* Addressed review comments
* Addressed review comment
Some queries like `REPLACE INTO ... SELECT TIME_PARSE("__time") AS __time FROM ...`
fail at the Calcite layer because any column with name `__time` is considered to be of
type `SqlTypeName.TIMESTAMP`.
Changes:
- Modify `RowSignatures.toRelDataType()` so that the type of `__time` column
is determined by the RowSignature's type.
* Automatic sizing for GroupBy dictionary sizes.
Merging and selector dictionary sizes currently both default to 100MB.
This is not optimal, because it can lead to OOM on small servers and
insufficient resource utilization on larger servers. It also invites
end users to try to tune it when queries run out of dictionary space,
which can make things worse if the end user sets it to too high.
So, this patch:
- Adds automatic tuning for selector and merge dictionaries. Selectors
use up to 15% of the heap and merge buffers use up to 30% of the heap
(aggregate across all queries).
- Updates out-of-memory error messages to emphasize enabling disk
spilling vs. increasing memory parameters. With the memory parameters
automatically sized, it is more likely that an end user will get
benefit from enabling disk spilling.
- Removes the query context parameters that allow lowering of configured
dictionary sizes. These complicate the calculation, and I don't see a
reasonable use case for them.
* Adjust tests.
* Review adjustments.
* Additional comment.
* Remove unused import.
* Preserve column order in DruidSchema, SegmentMetadataQuery.
Instead of putting columns in alphabetical order. This is helpful
because it makes query order better match ingestion order. It also
allows tools, like the reindexing flow in the web console, to more
easily do follow-on ingestions using a column order that matches the
pre-existing column order.
We prefer the order from the latest segments. The logic takes all
columns from the latest segments in the order they appear, then adds
on columns from older segments after those.
* Additional test adjustments.
* Adjust imports.
* Frame format for data transfer and short-term storage.
As we move towards query execution plans that involve more transfer
of data between servers, it's important to have a data format that
provides for doing this more efficiently than the options available to
us today.
This patch adds:
- Columnar frames, which support fast querying.
- Row-based frames, which support fast sorting via memory comparison
and fast whole-row copies via memory copying.
- Frame files, a container format that can be stored on disk or
transferred between servers.
The idea is we should use row-based frames when data is expected to
be sorted, and columnar frames when data is expected to be queried.
The code in this patch is not used in production yet. Therefore, the
patch involves minimal changes outside of the org.apache.druid.frame
package. The main ones are adjustments to SqlBenchmark to add benchmarks
for queries on frames, and the addition of a "forEach" method to Sequence.
* Fixes based on tests, static analysis.
* Additional fixes.
* Skip DS mapping tests on JDK 14+
* Better JDK checking in tests.
* Fix imports.
* Additional comment.
* Adjustments from code review.
* Update test case.
* Add EIGHT_HOUR into possible list of Granularities.
* Add the missing definition.
* fix test.
* Fix another test.
* Stylecheck finally passed.
Co-authored-by: Didip Kerabat <didip@apple.com>
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`
SQL expressions such as those containing `MV_FILTER_ONLY` and `MV_FILTER_NONE`
are planned as specialized virtual columns instead of the default `expression`-type virtual columns.
This commit adds a new context parameter to force the `expression`-type virtual columns.
Changes
- Add query context param `forceExpressionVirtualColumns`
- Use context param to determine if specialized virtual columns should be used or not
- Moved some tests into `CalciteExplainQueryTest`
* Add TIME_IN_INTERVAL SQL operator.
The operator is implemented as a convertlet rather than an
OperatorConversion, because this allows it to be equivalent to using
the >= and < operators directly.
* SqlParserPos cannot be null here.
* Remove unused import.
* Doc updates.
* Add words to dictionary.
True, false, and null have different meanings: true/false mean "legacy"
and "not legacy"; null means use the default set by ScanQueryConfig.
So, we need to respect this in the JsonIgnore setup.
* Remove null and empty fields from native queries
* Test fixes
* Attempted IT fix.
* Revisions from review comments
* Build fixes resulting from changes suggested by reviews
* IT fix for changed segment size
Fixes an issue where sql query request logs do not include the default query context
values set via `druid.query.default.context.xyz` runtime properties.
# Change summary
* Inject `DefaultQueryConfig` into `SqlLifecycleFactory`
* Add params from `DefaultQueryConfig` to the query context in `SqlLifecycle`
# Description
- This change does not affect query execution. This is because the
`DefaultQueryConfig` was already being used in `QueryLifecycle`,
which is initialized when the SQL is translated to a native query.
- This also handles any potential use case where a context parameter should be
handled at the SQL stage itself.
zachjsh
Paul Rogers
Clint Wylie
AmatyaAvadhanula
imply-cheddar
somu-imply
Laksh Singla
Adarsh Sanjeev
Rohan Garg
Kashif Faraz
Gian Merlino
Abhishek Agarwal
Sam Rash
Frank Chen
Karan Kumar
Gian Merlino
Maytas Monsereenusorn
Didip Kerabat
Tejaswini Bandlamudi
TSFenwick