* apache#12063 Ease of hidding sensitive properties from /status/properties endpoint
* apache#12063 Ease of hidding sensitive properties from /status/properties endpoint
* apache#12063 Ease of hidding sensitive properties from /status/properties endpoint
using one property for hiding properties, updated the index.md to document hiddenProperties
* apache#12063 Ease of hidding sensitive properties from /status/properties endpoint
Added java docs
* apache#12063 Ease of hidding sensitive properties from /status/properties endpoint
Add "password", "key", "token", "pwd" as default druid.server.hiddenProperties
fixed typo and removed redundant space
Co-authored-by: zemin <zemin.piao@adyen.com>
* 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
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.
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
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.
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.
* Add check for eternity time segment to SqlSegmentsMetadataQuery
* Add check for half eternities
* Add multiple segments test
* Add failing test to document known issue
* 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>
* Use nonzero default value of maxQueuedBytes.
The purpose of this parameter is to prevent the Broker from running out
of memory. The prior default is unlimited; this patch changes it to a
relatively conservative 25MB.
This may be too low for larger clusters. The risk is that throughput
can decrease for queries with large resultsets or large amounts of intermediate
data. However, I think this is better than the risk of the prior default, which
is that these queries can cause the Broker to go OOM.
* Alter calculation.
Sysmonitor stats (mem, fs, disk, net, cpu, swap, sys, tcp) are reported by all Druid processes, including Peons that are ephemeral in nature. Since Peons always run on the same host as the MiddleManager that spawned them and is unlikely to change, the SyMonitor metrics emitted by Peon are merely duplicates. This is often not a problem except when machines are super-beefy. Imagine a 64-core machine and 32 workers running on this machine. now you will have each Peon reporting metrics for each core. that's an increase of (32 * 64)x in the number of metrics. This leads to a metric explosion.
This PR updates MetricsModule to check node role running while registering SysMonitor and not to load any existing SysMonitor$Stats.
Few indexing tasks register RealtimeMetricsMonitor or TaskRealtimeMetricsMonitor with the process’s MonitorScheduler when they start. These monitors never unregister themselves (they always return true, they'd need to return false to unregister). Each of these monitors emits a set of metrics once every druid.monitoring.emissionPeriod.
As a result, after executing several tasks for a while, Indexer emits metrics of these tasks even after they're long gone.
Proposed Solution
Since one should be able to obtain the last round of ingestion metrics after the task unregisters the monitor, introducing lastRoundMetricsToBePushed variable to keep track of the same and overriding the AbstractMonitor.monitor method in RealtimeMetricsMonitor, TaskRealtimeMetricsMonitor to implement the new logic.
* initial commit of bucket dimensions for metrics
return counts of segments that have rowcount in a bucket size for a datasource
return average value of rowcount per segment in a datasource
added unit test
naming could use a lot of work
buckets right now are not finalized
added javadocs
altered metrics.md
* fix checkstyle issues
* addressed review comments
add monitor test
move added functionality to new monitor
update docs
* address comments
renamed monitor
handle tombstones better
update docs
added javadocs
* Add support for tombstones in the segment distribution
* undo changes to tombstone segmentizer factory
* fix accidental whitespacing changes
* address comments regarding metrics documentation
and rename variable to be more accurate
* fix tests
* fix checkstyle issues
* fix broken test
* undo removal of timeout
* 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.
The expiry timeout is compared against the current time but the condition is reversed.
This means that as soon as a supervisor task finishes, its partitions are cleaned up,
irrespective of the specified `intermediaryPartitionTimeout` period.
After these changes, the `intermediaryPartitionTimeout` will start getting honored.
Changes
* Fix the condition
* Add tests to verify the new correct behaviour
* Reduce the default expiry timeout from P1D to PT5M
to retain current behaviour in case of default configs.
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`
* 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
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.
* Clean up query contexts
Uses constants in place of literal strings for context keys.
Moves some QueryContext methods to QueryContexts for reuse.
* Revisions from review comments
* Add QoSFilters first in the chain.
When a request is suspended and later resumed due to QoS constraints,
its filter chain is restarted. Placing QoSFilters first in the chain
avoids double-execution of other filters.
Fixes an issue where requests deferred by QoS would report 403 Forbidden
due to double-execution of SecuritySanityCheckFilter.
* Smaller changes.
* Add QoS filters in BaseJettyTest.
* Remove unused parameter.
* SqlSegmentsMetadataQuery: Fix OVERLAPS for wide target segments.
Segments with endpoints prior to year 0 or after year 9999 may overlap
the search intervals but not match the generated SQL conditions. So, we
need to add an additional OR condition to catch these.
I checked a real, live MySQL metadata store to confirm that the query
still uses metadata store indexes. It does.
* Add comments.
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.
Adds a default implementation of getQueryContext, which was added to the Query interface in #12396. Query is marked with @ExtensionPoint, and lately we have been trying to be less volatile on these interfaces by providing default implementations to be more chill for extension writers.
The way this default implementation is done in this PR is a bit strange due to the way that getQueryContext is used (mutated with system default and system generated keys); the default implementation has a specific object that it returns, and I added another temporary default method isLegacyContext that checks if the getQueryContext returns that object or not. If not, callers fall back to using getContext and withOverriddenContext to set these default and system values.
I am open to other ideas as well, but this way should work at least without exploding, and added some tests to ensure that it is wired up correctly for QueryLifecycle, including the context authorization stuff.
The added test shows the strange behavior if query context authorization is enabled, mainly that the system default and system generated query context keys also need to be granted as permissions for things to function correctly. This is not great, so I mentioned it in the javadocs as well. Not sure if it needs to be called out anywhere else.
* Emit state of replace and append for native batch tasks
* Emit count of one depending on batch ingestion mode (APPEND, OVERWRITE, REPLACE)
* Add metric to compaction job
* Avoid null ptr exc when null emitter
* Coverage
* Emit tombstone & segment counts
* Tasks need a type
* Spelling
* Integrate BatchIngestionMode in batch ingestion tasks functionality
* Typos
* Remove batch ingestion type from metric since it is already in a dimension. Move IngestionMode to AbstractTask to facilitate having mode as a dimension. Add metrics to streaming. Add missing coverage.
* Avoid inner class referenced by sub-class inspection. Refactor computation of IngestionMode to make it more robust to null IOConfig and fix test.
* Spelling
* Avoid polluting the Task interface
* Rename computeCompaction methods to avoid ambiguous java compiler error if they are passed null. Other minor cleanup.
Issue:
Even though `CompactionTuningConfig` allows a `maxColumnsToMerge` config
(to optimize memory usage, particulary for datasources with many dimensions),
the corresponding client object `ClientCompactionTaskQueryTuningConfig`
(used by the coordinator duty `CompactSegments` to trigger auto-compaction)
does not contain this field. Thus, the value of `maxColumnsToMerge` specified
in any datasource compaction config is ignored.
Changes:
- Add field `maxColumnsToMerge` in `ClientCompactionTaskQueryTuningConfig`
and `UserCompactionTaskQueryTuningConfig`
- Fix tests
* 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.
Allow a Druid cluster to kill segments whose interval_end is a date in the future. This can be done by setting druid.coordinator.kill.durationToRetain to a negative period. For example PT-24H would allow segments to be killed if their interval_end date was 24 hours or less into the future at the time that the kill task is generated by the system.
A cluster operator can also disregard the druid.coordinator.kill.durationToRetain entirely by setting a new configuration, druid.coordinator.kill.ignoreDurationToRetain=true. This ignores interval_end date when looking for segments to kill, and instead is capable of killing any segment marked unused. This new configuration is off by default, and a cluster operator should fully understand and accept the risks if they enable it.
zemin
Abhishek Agarwal
Clint Wylie
Adam Peck
Santosh Pingale
Paul Rogers
Bartosz Mikulski
Adarsh Sanjeev
Rohan Garg
Karan Kumar
Gian Merlino
Gian Merlino
Atul Mohan
Tejaswini Bandlamudi
zachjsh
TSFenwick
Kashif Faraz
AmatyaAvadhanula
Lucas Capistrant
Agustin Gonzalez
superivaj