* 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.
This adds min/max functions for CompressedBigDecimal. It exposes these
functions via sql (BIG_MAX, BIG_MIN--see the SqlAggFunction
implementations).
It also includes various bug fixes and cleanup to the original
CompressedBigDecimal code include the AggregatorFactories. Various null
handling was improved.
Additional test cases were added for both new and existing code
including a base test case for AggregationFactories. Other tests common
across sum,min,max may be refactored also to share the varoius cases in
the future.
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
Optimizes the compareTo() function in
CompressedBigDecimal. It directly compares the int[] rather than
creating BigDecimal objects and using its compareTo.
It handles unequal sized CBDs, but does require
the scales to match.
1. remove unnecessary generic type from CompressedBigDecimal
2. support Number input types
3. support aggregator reading supported input types directly (uningested
data)
4. fix scaling bug in buffer aggregator
* prometheus-emitter supports sending metrics to pushgateway regularly and continuously
* spell check fix
* Optimization variable name and related documents
* Update docs/development/extensions-contrib/prometheus.md
OK, it looks more conspicuous
Co-authored-by: Frank Chen <frankchen@apache.org>
* Update doc
* Update docs/development/extensions-contrib/prometheus.md
Co-authored-by: Frank Chen <frankchen@apache.org>
* When PrometheusEmitter is closed, close the scheduler
* Ensure that registeredMetrics is thread safe.
* Local variable name optimization
* Remove unnecessary white space characters
Co-authored-by: Frank Chen <frankchen@apache.org>
Compressed Big Decimal is an extension which provides support for
Mutable big decimal value that can be used to accumulate values
without losing precision or reallocating memory. This type helps in
absolute precision arithmetic on large numbers in applications,
where greater level of accuracy is required, such as financial
applications, currency based transactions. This helps avoid rounding
issues where in potentially large amount of money can be lost.
Accumulation requires that the two numbers have the same scale,
but does not require that they are of the same size. If the value
being accumulated has a larger underlying array than this value
(the result), then the higher order bits are dropped, similar to what
happens when adding a long to an int and storing the result in an
int. A compressed big decimal that holds its data with an embedded
array.
Compressed big decimal is an absolute number based complex type
based on big decimal in Java. This supports all the functionalities
supported by Java Big Decimal. Java Big Decimal is not mutable in
order to avoid big garbage collection issues. Compressed big decimal
is needed to mutate the value in the accumulator.
* Fixing RACE in HTTP remote task Runner
* Changes in the interface
* Updating documentation
* Adding test cases to SwitchingTaskLogStreamer
* Adding more tests
Fixes KafkaEmitter not emitting queryType for a native query. The Event to JSON serialization was extracted to the external class: EventToJsonSerializer. This was done to simplify the testing logic for the serialization as well as extract the responsibility of serialization to the separate class.
The logic builds ObjectNode incrementally based on the event .toMap method. Parsing each entry individually ensures that the Jackson polymorphic annotations are respected. Not respecting these annotation caused the missing of the queryType from output event.
* 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
* Introduce defaultOnDiskStorage config for groupBy
* add debug log to groupby query config
* Apply config change suggestion from review
* Remove accidental new lines
* update default value of new default disk storage config
* update debug log to have more descriptive text
* Make maxOnDiskStorage and defaultOnDiskStorage HumanRedadableBytes
* improve test coverage
* Provide default implementation to new default method on advice of reviewer
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.
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.
Often users are submitting queries, and ingestion specs that work only if the relevant extension is not loaded. However, the error is too technical for the users and doesn't suggest them to check for missing extensions. This PR modifies the error message so users can at least check their settings before assuming that the error is because of a bug.
* 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.
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.
The latest version of Error Prone now requires Java 11. Upgrading means we can
remove a lot of the maven profile complexity required to run checks with Java 8.
This also requires switching our strict build to use Java 11.
* update error-prone to 2.11
* remove need for specific maven profiles for Java 8 and Java 15
* fix additional Error Prone warnings with Java 11
* update strict build to use Java 11
* 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
* 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
* Refactor ResponseContext
Fixes a number of issues in preparation for request trailers
and the query profile.
* Converts keys from an enum to classes for smaller code
* Wraps stored values in functions for easier capture for other uses
* Reworks the "header squeezer" to handle types other than arrays.
* Uses metadata for visibility, and ability to compress,
to replace ad-hoc code.
* Cleans up JSON serialization for the response context.
* Other miscellaneous cleanup.
* Handle unknown keys in deserialization
Also, make "Visibility" into a boolean.
* Revised comment
* Renamd variable
Druid currently has 2 serverViews, regular serverView and filtered serverView. The regular serverView is used to monitor all segment announcements from all data nodes (historicals, tasks, indexers). The filtered serverView is used when you want to watch segment announcements from particular tiers. Since these server views keep track of different sets of druidServers and segments in memory, they should be maintained separately. However, they currently share the same name for their executorService, which can cause confusion and make debugging harder especially in the broker since it is using both serverViews, the filtered view for normal query processing and the regular view to serve the servers table (I'm unsure whether this is intended or whether this is a good behavior). This PR changes it to a more obvious name.
This PR also removes SingleServerInventoryView. This view was deprecated a long time ago and has not been documented at least since 0.13 (#6127). I also don't think this can be better in any case than BatchServerInventoryView. Finally, I merged AbstractCuratorServerInventoryView and BatchServerInventoryView as we no longer need AbstractCuratorServerInventoryView after SingleServerInventoryView is removed.
* Make nodeRole available during binding; add support for dynamic registration of DruidService
* fix checkstyle and test
* fix customRole test
* address comments
* add more javadoc
* Consolidate a bunch of ad-hoc segments metadata SQL; fix some bugs.
This patch gathers together a variety of SQL from SqlSegmentsMetadataManager
and IndexerSQLMetadataStorageCoordinator into a new class SqlSegmentsMetadataQuery.
It focuses on SQL related to retrieving segment payloads and marking
segments used and unused.
In addition to cleaning up the code a bit, this patch also fixes a bug
with years before 0 or after 9999. The prior SQL did not work properly
because dates outside this range cannot be compared as strings. The new
code does work for these far-past and far-future years.
So, if you're ever interested in using Druid to analyze things from
ancient Babylon, you better apply this patch first!
* Fix test compiling.
* Fixes and improvements.
* Fix forbidden API.
* Additional fixes.
Gian Merlino
Paul Rogers
zachjsh
Sam Rash
Frank Chen
Abhishek Agarwal
Jonathan Wei
DENNIS
Frank Chen
Didip Kerabat
senthilkv
Karan Kumar
Bartosz Mikulski
Lucas Capistrant
Jianhuan Liu
Rohan Garg
AmatyaAvadhanula
dependabot[bot]
Jihoon Son
syacobovitz
Aurélien Dunand
Xavier Léauté
Clint Wylie
JoyKing
Uwe Schindler
Ivan Vankovich