Currently when we submit a task to druid and number of currently active tasks has already reached (druid.indexer.queue.maxSize) then 500 ISE is thrown as per shown in the screenshot in #15380.
This fix will return HTTP 429 Too Many Requests(with proper error message) instead of 500 ISE, when we submit a task and queueSize has reached.
* Fix NPE caused by realtime segment closing race, fix possible missing-segment retry bug.
Fixes#12168, by returning empty from FireHydrant when the segment is
swapped to null. This causes the SinkQuerySegmentWalker to use
ReportTimelineMissingSegmentQueryRunner, which causes the Broker to look
for the segment somewhere else.
In addition, this patch changes SinkQuerySegmentWalker to acquire references
to all hydrants (subsegments of a sink) at once, and return a
ReportTimelineMissingSegmentQueryRunner if *any* of them could not be acquired.
I suspect, although have not confirmed, that the prior behavior could lead to
segments being reported as missing even though results from some hydrants were
still included.
* Some more test coverage.
* Make numCorePartitions as 0 in the TombstoneShardSpec.
* fix up test
* Add tombstone core partition tests
* review comment
* Need to register the test shard type to make jackson happy
This patch introduces a param snapshotTime in the iceberg inputsource spec that allows the user to ingest data files associated with the most recent snapshot as of the given time. This helps the user ingest data based on older snapshots by specifying the associated snapshot time.
This patch also upgrades the iceberg core version to 1.4.1
Fixed the following flaky tests:
org.apache.druid.math.expr.ParserTest#testApplyFunctions
org.apache.druid.math.expr.ParserTest#testSimpleMultiplicativeOp1
org.apache.druid.math.expr.ParserTest#testFunctions
org.apache.druid.math.expr.ParserTest#testSimpleLogicalOps1
org.apache.druid.math.expr.ParserTest#testSimpleAdditivityOp1
org.apache.druid.math.expr.ParserTest#testSimpleAdditivityOp2
The above mentioned tests have been reported as flaky (tests assuming deterministic implementation of a non-deterministic specification ) when ran against the NonDex tool.
The tests contain assertions (Assertion 1 & Assertion 2) that compare an ArrayList created from a HashSet using the ArrayList() constructor with another List. However, HashSet does not guarantee the ordering of elements and thus resulting in these flaky tests that assume deterministic implementation of HashSet. Thus, when the NonDex tool shuffles the HashSet elements, it results in the test failures:
Co-authored-by: ythorat2 <ythorat2@illinois.edu>
The TaskQueue maintains a map of active task ids to tasks, which can be utilized to get active task payloads, before falling back to the metadata store.
There is a problem with Quantiles sketches and KLL Quantiles sketches.
Queries using the histogram post-aggregator fail if:
- the sketch contains at least one value, and
- the values in the sketch are all equal, and
- the splitPoints argument is not passed to the post-aggregator, and
- the numBins argument is greater than 2 (or not specified, which
leads to the default of 10 being used)
In that case, the query fails and returns this error:
{
"error": "Unknown exception",
"errorClass": "org.apache.datasketches.common.SketchesArgumentException",
"host": null,
"errorCode": "legacyQueryException",
"persona": "OPERATOR",
"category": "RUNTIME_FAILURE",
"errorMessage": "Values must be unique, monotonically increasing and not NaN.",
"context": {
"host": null,
"errorClass": "org.apache.datasketches.common.SketchesArgumentException",
"legacyErrorCode": "Unknown exception"
}
}
This behaviour is undesirable, since the caller doesn't necessarily
know in advance whether the sketch has values that are diverse
enough. With this change, the post-aggregators return [N, 0, 0...]
instead of crashing, where N is the number of values in the sketch,
and the length of the list is equal to numBins. That is what they
already returned for numBins = 2.
Here is an example of a query that would fail:
{"queryType":"timeseries",
"dataSource": {
"type": "inline",
"columnNames": ["foo", "bar"],
"rows": [
["abc", 42.0],
["def", 42.0]
]
},
"intervals":["0000/3000"],
"granularity":"all",
"aggregations":[
{"name":"the_sketch", "fieldName":"bar", "type":"quantilesDoublesSketch"}],
"postAggregations":[
{"name":"the_histogram",
"type":"quantilesDoublesSketchToHistogram",
"field":{"type":"fieldAccess","fieldName":"the_sketch"},
"numBins": 3}]}
I believe this also fixes issue #10585.
* Fix capacity response in mm-less ingestion (#14888)
Changes:
- Fix capacity response in mm-less ingestion.
- Add field usedClusterCapacity to the GET /totalWorkerCapacity response.
This API should be used to get the total ingestion capacity on the overlord.
- Remove method `isK8sTaskRunner` from interface `TaskRunner`
* Using Map to perform comparison
* Minor Change
---------
Co-authored-by: George Shiqi Wu <george.wu@imply.io>
Saw bug where MSQ controller task would continue to hold the task slot even after cancel was issued.
This was due to a deadlock created on work launch. The main thread was waiting for tasks to spawn and the cancel thread was waiting for tasks to finish.
The fix was to instruct the MSQWorkerTaskLauncher thread to stop creating new tasks which would enable the main thread to unblock and release the slot.
Also short circuited the taskRetriable condition. Now the check is run in the MSQWorkerTaskLauncher thread as opposed to the main event thread loop. This will result in faster task failure in case the task is deemed to be non retriable.
* MSQ generates tombstones honoring the query's granularity.
This change tweaks to only account for the infinite-interval tombstones.
For finite-interval tombstones, the MSQ query granualrity will be used
which is consistent with how MSQ works.
* more tests and some cleanup.
* checkstyle
* comment edits
* Throw TooManyBuckets fault based on review; add more tests.
* Add javadocs for both methods on reconciling the methods.
* review: Move testReplaceTombstonesWithTooManyBucketsThrowsException to MsqFaultsTest
* remove unused imports.
* Move TooManyBucketsException to indexing package for shared exception handling.
* lower max bucket for tests and fixup count
* Advance and count the iterator.
* checkstyle
* + Fix for Flaky Test
* + Replacing TreeMap with LinkedHashMap
* + Changing data structure from LinkedHashMap to HashMap
* Fixed flaky test in S3DataSegmentPusherConfigTest.testSerializationValidatingMaxListingLength
* Minor Changes
In pull request #14985, a bug was introduced where periodic refresh would skip rebuilding a datasource's schema after encountering a non-existent datasource. This resulted in remaining datasources having stale schema information.
This change addresses the bug and adds a unit test to validate the refresh mechanism's behaviour when a datasource is removed, and other datasources have schema changes.
Currently, the redis-cache extension uses Jedis 2.9.0, which was released over seven years ago and is no longer listed in the official support matrix. This patch upgrades it to ensure the compatibility with the recent version of Redis and make future upgrades easier, including:
Upgrade Jedis to v5.0.2, the latest version at this writing, and address the API changes and dependency version mismatch.
Replace mock-jedis with jedis-mock, since the former has not been actively maintained any longer and not compatible with recent versions of Jedis.
Lately, Query IT has been failing due to historical server running out of memory (OOM).
We are investigating the historical heap dump from the test. Until the issue is resolved, we are increasing the heap size of historical server.
* Add a unit test that fails when used segments with too many intervals are retrieved.
- This is a failing test case that needs to be ignored.
* Batch the intervals (use 100 as it's consistent with batching in other places).
* move the filtering inside the batch
* Account for limit cross the batch splits.
* Adjustments
* Fixup and add tests
* small refactor
* add more tests.
* remove wrapper.
* Minor edits
* assert out of range
Historical OOMs were not getting dumped into /shared/logs because common JVM flags will override service-specific JVM flags. This PR fixes that and also removes unnecessary overrides in historical.
In the current design, brokers query both data nodes and tasks to fetch the schema of the segments they serve. The table schema is then constructed by combining the schemas of all segments within a datasource. However, this approach leads to a high number of segment metadata queries during broker startup, resulting in slow startup times and various issues outlined in the design proposal.
To address these challenges, we propose centralizing the table schema management process within the coordinator. This change is the first step in that direction. In the new arrangement, the coordinator will take on the responsibility of querying both data nodes and tasks to fetch segment schema and subsequently building the table schema. Brokers will now simply query the Coordinator to fetch table schema. Importantly, brokers will still retain the capability to build table schemas if the need arises, ensuring both flexibility and resilience.
Sachidananda Maharana
Zoltan Haindrich
Vadim Ogievetsky
Kashif Faraz
Jill Osborne
317brian
Gian Merlino
Abhishek Radhakrishnan
Clint Wylie
Atul Mohan
Yashdeep Thorat
AmatyaAvadhanula
Charles Smith
Karan Kumar
Magnus Henoch
Krishna Anandan
Rishabh Singh
Adarsh Sanjeev
dependabot[bot]
YongGang
George Shiqi Wu
Kengo Seki
Pranav
17px
nasuiyile
HudsonShi
Abhishek Agarwal
Laksh Singla