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
AmatyaAvadhanula
Gian Merlino
Adarsh Sanjeev
somu-imply
Paul Rogers
Laksh Singla
hnakamor
zachjsh
Tejaswini Bandlamudi
Jonathan Wei
Frank Chen
Abhishek Agarwal
Ellen Shen
Atul Mohan
imply-cheddar
Clint Wylie
Alexander Saydakov
Junge
Karan Kumar
William Hyun
David Palmer
Hamish Ball
Rui Chen
Didip Kerabat
Dongjoon Hyun
Kashif Faraz
Lucas Capistrant
MC-JY
Gian Merlino
Jihoon Son
PJ Fanning
Rohan Garg
AmatyaAvadhanula
Kyle Larose
Parag Jain
Laksh Singla
Xavier Léauté
Suneet Saldanha
Agustin Gonzalez
lokesh-lingarajan
zhangyue19921010
Maytas Monsereenusorn
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