They were not previously loaded because supportsQueries was false.
This patch sets supportsQueries to true, and clarifies in Task
javadocs that supportsQueries can be true for tasks that aren't
directly queryable over HTTP.
* Limit select results in MSQ
* reduce number of files in test
* add truncated flag
* avoid materializing select results to list, use iterable instead
* javadocs
* fix kafka input format reader schema discovery and partial schema discovery to actually work right, by re-using dimension filtering logic of MapInputRowParser
changes:
* auto columns no longer participate in generic 'null column' handling, this was a mistake to try to support and caused ingestion failures due to mismatched ColumnFormat, and will be replaced in the future with nested common format constant column functionality (not in this PR)
* fix bugs with auto columns which contain empty objects, empty arrays, or primitive types mixed with either of these empty constructs
* fix bug with bound filter when upper is null equivalent but is strict
* Add INFORMATION_SCHEMA.ROUTINES to expose Druid operators and functions.
* checkstyle
* remove IS_DETERMISITIC.
* test
* cleanup test
* remove logs and simplify
* fixup unit test
* Add docs for INFORMATION_SCHEMA.ROUTINES table.
* Update test and add another SQL query.
* add stuff to .spelling and checkstyle fix.
* Add more tests for custom operators.
* checkstyle and comment.
* Some naming cleanup.
* Add FUNCTION_ID
* The different Calcite function syntax enums get translated to FUNCTION
* Update docs.
* Cleanup markdown table.
* fixup test.
* fixup intellij inspection
* Review comment: nullable column; add a function to determine function syntax.
* More tests; add non-function syntax operators.
* More unit tests. Also add a separate test for DruidOperatorTable.
* actually just validate non-zero count.
* switch up the order
* checkstyle fixes.
This PR adds the following to the ATTRIBUTES column in the explain plan output:
- partitionedBy
- clusteredBy
- replaceTimeChunks
This PR leverages the work done in #14074, which added a new column ATTRIBUTES
to encapsulate all the statement-related attributes.
Changes
- Add a `DruidException` which contains a user-facing error message, HTTP response code
- Make `EntryExistsException` extend `DruidException`
- If metadata store max_allowed_packet limit is violated while inserting a new task, throw
`DruidException` with response code 400 (bad request) to prevent retries
- Add `SQLMetadataConnector.isRootCausePacketTooBigException` with impl for MySQL
The class apparently only exists to add a toString()
method to Indexes, which basically just crashes any debugger
on any meaningfully sized index. It's a pointless
abstract class that basically only causes pain.
Changes:
- Add a timeout of 1 minute to resultFuture.get() in `CostBalancerStrategy.chooseBestServer`.
1 minute is the typical time for a full coordinator run and is more than enough time for cost
computations of a single segment.
- Raise an alert if an exception is encountered while computing costs and if the executor has
not been shutdown. This is because a shutdown is intentional and does not require an alert.
* Fix EarliestLatestBySqlAggregator signature; Include function name for all signatures.
* Single quote function signatures, space between args and remove \n.
* fixup UT assertion
The same aggregator can have two output names for a SQL like:
INSERT INTO foo
SELECT x, COUNT(*) AS y, COUNT(*) AS z
FROM t
GROUP BY 1
PARTITIONED BY ALL
In this case, the SQL planner will create a query with a single "count"
aggregator mapped to output names "y" and "z". The prior MSQ code did
not properly handle this case, instead throwing an error like:
Expected single output for query column[a0] but got [[1, 2]]
* Fix read timed out failures and remove containers before test
* remove containers before loading images
* add labels to IT docker containers, download stable minio docker image release instead of latest
In this PR, we are enhancing KafkaEmitter, to emit metadata about published segments (SegmentMetadataEvent) into a Kafka topic. This segment metadata information that gets published into Kafka, can be used by any other downstream services to query Druid intelligently based on the segments published. The segment metadata gets published into kafka topic in json string format similar to other events.
The sampler API returns a `400 bad request` response if it encounters a `SamplerException`.
Otherwise, it returns a generic `500 Internal server error` response, with the message
"The RuntimeException could not be mapped to a response, re-throwing to the HTTP container".
This commit updates `RecordSupplierInputSource` to handle all types of exceptions instead of just
`InterruptedException`and wrap them in a `SamplerException` so that the actual error is
propagated back to the user.
It was found that several supported tasks / input sources did not have implementations for the methods used by the input source security feature, causing these tasks and input sources to fail when used with this feature. This pr adds the needed missing implementations. Also securing the sampling endpoint with input source security, when enabled.
### Description
This change allows for consideration of the input format and compression when computing how to split the input files among available tasks, in MSQ ingestion, when considering the value of the `maxInputBytesPerWorker` query context parameter. This query parameter allows users to control the maximum number of bytes, with granularity of input file / object, that ingestion tasks will be assigned to ingest. With this change, this context parameter now denotes the estimated weighted size in bytes of the input to split on, with consideration for input format and compression format, rather than the actual file size, reported by the file system. We assume uncompressed newline delimited json as a baseline, with scaling factor of `1`. This means that when computing the byte weight that a file has towards the input splitting, we take the file size as is, if uncompressed json, 1:1. It was found during testing that gzip compressed json, and parquet, has scale factors of `4` and `8` respectively, meaning that each byte of data is weighted 4x and 8x respectively, when computing input splits. This weighted byte scaling is only considered for MSQ ingestion that uses either LocalInputSource or CloudObjectInputSource at the moment. The default value of the `maxInputBytesPerWorker` query context parameter has been updated from 10 GiB, to 512 MiB
George Shiqi Wu
Clint Wylie
Gian Merlino
Maytas Monsereenusorn
Laksh Singla
Pranav
Alexander Saydakov
Vadim Ogievetsky
Abhishek Radhakrishnan
Rishabh Singh
Adarsh Sanjeev
Kashif Faraz
John Gozde
Benedict Jin
imply-cheddar
317brian
Atul Mohan
Soumyava
Tejaswini Bandlamudi
Abhishek Agarwal
Katya Macedo
Harini Rajendran
Andreas Maechler
zachjsh