* SQL test framework extensions
* Capture planner artifacts: logical plan, etc.
* Planner test builder validates the logical plan
* Validation for the SQL resut schema (we already have
validation for the Druid row signature)
* Better Guice integration: properties, reuse Guice modules
* Avoid need for hand-coded expr, macro tables
* Retire some of the test-specific query component creation
* Fix query log hook race condition
Co-authored-by: Paul Rogers <progers@apache.org>
* discover nested columns when using nested column indexer for schemaless
* move useNestedColumnIndexerForSchemaDiscovery from AppendableIndexSpec to DimensionsSpec
Much improved table functions
* Revises properties, definitions in the catalog
* Adds a "table function" abstraction to model such functions
* Specific functions for HTTP, inline, local and S3.
* Extended SQL types in the catalog
* Restructure external table definitions to use table functions
* EXTEND syntax for Druid's extern table function
* Support for array-valued table function parameters
* Support for array-valued SQL query parameters
* Much new documentation
* Kinesis: More robust default fetch settings.
1) Default recordsPerFetch and recordBufferSize based on available memory
rather than using hardcoded numbers. For this, we need an estimate
of record size. Use 10 KB for regular records and 1 MB for aggregated
records. With 1 GB heaps, 2 processors per task, and nonaggregated
records, recordBufferSize comes out to the same as the old
default (10000), and recordsPerFetch comes out slightly lower (1250
instead of 4000).
2) Default maxRecordsPerPoll based on whether records are aggregated
or not (100 if not aggregated, 1 if aggregated). Prior default was 100.
3) Default fetchThreads based on processors divided by task count on
Indexers, rather than overall processor count.
4) Additionally clean up the serialized JSON a bit by adding various
JsonInclude annotations.
* Updates for tests.
* Additional important verify.
* Quote and escape table, key and column names.
* fix typo.
* More select statements.
* Derby lookup tests create quoted identifiers so it's compatible.
* Use Stringutils.replace() utility.
* quote the filter string.
* Squish doubly quote usage into a single function.
* Add parameterized test with reserved identifiers.
* few changes.
* Addition of NaiveSortMaker and Default implementation
Add the NaiveSortMaker which makes a sorter
object and a default implementation of the
interface.
This also allows us to plan multiple different window
definitions on the same query.
* Validate response headers and fix exception logging
A class of QueryException were throwing away their
causes making it really hard to determine what's
going wrong when something goes wrong in the SQL
planner specifically. Fix that and adjust tests
to do more validation of response headers as well.
We allow 404s and 307s to be returned even without
authorization validated, but others get converted to 403
This PR expands `StringDimensionIndexer` to handle conversion of `byte[]` to base64 encoded strings, rather than the current behavior of calling java `toString`.
This issue was uncovered by a regression of sorts introduced by #13519, which updated the protobuf extension to directly convert stuff to java types, resulting in `bytes` typed values being converted as `byte[]` instead of a base64 string which the previous JSON based conversion created. While outputting `byte[]` is more consistent with other input formats, and preferable when the bytes can be consumed directly (such as complex types serde), when fed to a `StringDimensionIndexer`, it resulted in an ugly java `toString` because `processRowValsToUnsortedEncodedKeyComponent` is fed the output of `row.getRaw(..)`. Converting `byte[]` to a base64 string within `StringDimensionIndexer` is consistent with the behavior of calling `row.getDimension(..)` which does do this coercion (and why many tests on binary types appeared to be doing the expected thing).
I added some protobuf `bytes` tests, but they don't really hit the new `StringDimensionIndexer` behavior because they operate on the `InputRow` directly, and call `getDimension` to validate stuff. The parser based version still uses the old conversion mechanisms, so when not using a flattener incorrectly calls `toString` on the `ByteString`. I have encoded this behavior in the test for now, if we either update the parser to use the new flattener or just .. remove parsers we can remove this test stuff.
Follow up to #13520
Bytes processed are currently tracked for intermediate stages in MSQ ingestion.
This patch adds the capability to track the bytes processed by an MSQ controller
task while reading from an external input source or a segment source.
Changes:
- Track `processedBytes` for every `InputSource` read in `ExternalInputSliceReader`
- Update `ChannelCounters` with the above obtained `processedBytes` when incrementing
the input file count.
- Update task report structure in docs
The total input processed bytes can be obtained by summing the `processedBytes` as follows:
totalBytes = 0
for every root stage (i.e. a stage which does not have another stage as an input):
for every worker in that stage:
for every input channel: (i.e. channels with prefix "input", e.g. "input0", "input1", etc.)
totalBytes += processedBytes
* Add validation checks to worker chat handler apis
* Merge things and polishing the error messages.
* Minor error message change
* Fixing race and adding some tests
* Fixing controller fetching stats from wrong workers.
Fixing race
Changing default mode to Parallel
Adding logging.
Fixing exceptions not propagated properly.
* Changing to kernel worker count
* Added a better logic to figure out assigned worker for a stage.
* Nits
* Moving to existing kernel methods
* Adding more coverage
Co-authored-by: cryptoe <karankumar1100@gmail.com>
This commit adds a new class `InputStats` to track the total bytes processed by a task.
The field `processedBytes` is published in task reports along with other row stats.
Major changes:
- Add class `InputStats` to track processed bytes
- Add method `InputSourceReader.read(InputStats)` to read input rows while counting bytes.
> Since we need to count the bytes, we could not just have a wrapper around `InputSourceReader` or `InputEntityReader` (the way `CountableInputSourceReader` does) because the `InputSourceReader` only deals with `InputRow`s and the byte information is already lost.
- Classic batch: Use the new `InputSourceReader.read(inputStats)` in `AbstractBatchIndexTask`
- Streaming: Increment `processedBytes` in `StreamChunkParser`. This does not use the new `InputSourceReader.read(inputStats)` method.
- Extend `InputStats` with `RowIngestionMeters` so that bytes can be exposed in task reports
Other changes:
- Update tests to verify the value of `processedBytes`
- Rename `MutableRowIngestionMeters` to `SimpleRowIngestionMeters` and remove duplicate class
- Replace `CacheTestSegmentCacheManager` with `NoopSegmentCacheManager`
- Refactor `KafkaIndexTaskTest` and `KinesisIndexTaskTest`
Refactor DataSource to have a getAnalysis method()
This removes various parts of the code where while loops and instanceof
checks were being used to walk through the structure of DataSource objects
in order to build a DataSourceAnalysis. Instead we just ask the DataSource
for its analysis and allow the stack to rebuild whatever structure existed.
* Zero-copy local deep storage.
This is useful for local deep storage, since it reduces disk usage and
makes Historicals able to load segments instantaneously.
Two changes:
1) Introduce "druid.storage.zip" parameter for local storage, which defaults
to false. This changes default behavior from writing an index.zip to writing
a regular directory. This is safe to do even during a rolling update, because
the older code actually already handled unzipped directories being present
on local deep storage.
2) In LocalDataSegmentPuller and LocalDataSegmentPusher, use hard links
instead of copies when possible. (Generally this is possible when the
source and destination directory are on the same filesystem.)
The planner sets sqlInsertSegmentGranularity in its context when using
PARTITIONED BY, which sets it on every native query in the stack (as all
native queries for a SQL query typically have the same context).
QueryKit would interpret that as a request to configure bucketing for
all native queries. This isn't useful, as bucketing is only used for
the penultimate stage in INSERT / REPLACE.
So, this patch modifies QueryKit to only look at sqlInsertSegmentGranularity
on the outermost query.
As an additional change, this patch switches the static ObjectMapper to
use the processwide ObjectMapper for deserializing Granularities. Saves
an ObjectMapper instance, and ensures that if there are any special
serdes registered for Granularity, we'll pick them up.
1) Edited the TooManyBuckets error message to mention PARTITIONED BY
instead of segmentGranularity.
2) Added error-code-specific anchors in the docs.
3) Add information to various error codes in the docs about common
causes and solutions.
* Remove stray reference to fix OOM while merging sketches
* Update future to add result from executor service
* Update tests and address review comments
* Address review comments
* Moved mock
* Close threadpool on teardown
* Remove worker task cancel
SQL test framework extensions
* Capture planner artifacts: logical plan, etc.
* Planner test builder validates the logical plan
* Validation for the SQL resut schema (we already have
validation for the Druid row signature)
* Better Guice integration: properties, reuse Guice modules
* Avoid need for hand-coded expr, macro tables
* Retire some of the test-specific query component creation
* Fix query log hook race condition
* add faults tests for the multi stage query
* add too many parttiions fault
* add toomanyinputfilesfault
* programmatically generate the file
* refactor
* Trigger Build
https://github.com/apache/druid/pull/13027 PR replaces `filter` parameter with
`objectGlob` in ingestion input source. However, this will cause existing ingestion
jobs to fail if they are using a filter already. This PR adds old filter functionality
alongside objectGlob to preserve backward compatibility.
* we can read where we want to
we can leave your bounds behind
'cause if the memory is not there
we really don't care
and we'll crash this process of mine
* Attach IO error to parse error when we can't contact Avro schema registry.
The change in #12080 lost the original exception context. This patch
adds it back.
* Add hamcrest-core.
* Fix format string.
Main changes:
1) Convert SeekableStreamIndexTaskClient to an interface, move old code
to SeekableStreamIndexTaskClientSyncImpl, and add new implementation
SeekableStreamIndexTaskClientAsyncImpl that uses ServiceClient.
2) Add "chatAsync" parameter to seekable stream supervisors that causes
the supervisor to use an async task client.
3) In SeekableStreamSupervisor.discoverTasks, adjust logic to avoid making
blocking RPC calls in workerExec threads.
4) In SeekableStreamSupervisor generally, switch from Futures.successfulAsList
to FutureUtils.coalesce, so we can better capture the errors that occurred
with contacting individual tasks.
Other, related changes:
1) Add ServiceRetryPolicy.retryNotAvailable, which controls whether
ServiceClient retries unavailable services. Useful since we do not
want to retry calls unavailable tasks within the service client. (The
supervisor does its own higher-level retries.)
2) Add FutureUtils.transformAsync, a more lambda friendly version of
Futures.transform(f, AsyncFunction).
3) Add FutureUtils.coalesce. Similar to Futures.successfulAsList, but
returns Either instead of using null on error.
4) Add JacksonUtils.readValue overloads for JavaType and TypeReference.
Fixes inclusion of all stream partitions in all tasks.
The PR (Adds Idle feature to `SeekableStreamSupervisor` for inactive stream) - https://github.com/apache/druid/pull/13144 updates the resulting lag calculation map in `KafkaSupervisor` to include all the latest partitions from the stream to set the idle state accordingly rather than the previous way of lag calculation only for the partitions actively being read from the stream. This led to an explosion of metrics in lag reports in cases where 1000s of tasks per supervisor are present.
Changes:
- Add a new method to generate lags for only those partitions a single task is actively reading from while updating the Supervisor reports.
Adarsh Sanjeev
Rohan Garg
Laksh Singla
Clint Wylie
Paul Rogers
Gian Merlino
Maytas Monsereenusorn
Karan Kumar
Abhishek Radhakrishnan
imply-cheddar
Kashif Faraz
somu-imply
Abhishek Agarwal
AmatyaAvadhanula
xiaokang
Tejaswini Bandlamudi