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`
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.
* Use standard library to correctly glob and stop at the correct folder structure when filtering cloud objects.
Removed:
import org.apache.commons.io.FilenameUtils;
Add:
import java.nio.file.FileSystems;
import java.nio.file.PathMatcher;
import java.nio.file.Paths;
* Forgot to update CloudObjectInputSource as well.
* Fix tests.
* Removed unused exceptions.
* Able to reduced user mistakes, by removing the protocol and the bucket on filter.
* add 1 more test.
* add comment on filterWithoutProtocolAndBucket
* Fix lint issue.
* Fix another lint issue.
* Replace all mention of filter -> objectGlob per convo here:
https://github.com/apache/druid/pull/13027#issuecomment-1266410707
* fix 1 bad constructor.
* Fix the documentation.
* Don’t do anything clever with the object path.
* Remove unused imports.
* Fix spelling error.
* Fix incorrect search and replace.
* Addressing Gian’s comment.
* add filename on .spelling
* Fix documentation.
* fix documentation again
Co-authored-by: Didip Kerabat <didip@apple.com>
* Fixing RACE in HTTP remote task Runner
* Changes in the interface
* Updating documentation
* Adding test cases to SwitchingTaskLogStreamer
* Adding more tests
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.
* 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
Fixes#11297.
Description
Description and design in the proposal #11297
Key changed/added classes in this PR
*DataSegmentPusher
*ShuffleClient
*PartitionStat
*PartitionLocation
*IntermediaryDataManager
* DruidInputSource: Fix issues in column projection, timestamp handling.
DruidInputSource, DruidSegmentReader changes:
1) Remove "dimensions" and "metrics". They are not necessary, because we
can compute which columns we need to read based on what is going to
be used by the timestamp, transform, dimensions, and metrics.
2) Start using ColumnsFilter (see below) to decide which columns we need
to read.
3) Actually respect the "timestampSpec". Previously, it was ignored, and
the timestamp of the returned InputRows was set to the `__time` column
of the input datasource.
(1) and (2) together fix a bug in which the DruidInputSource would not
properly read columns that are used as inputs to a transformSpec.
(3) fixes a bug where the timestampSpec would be ignored if you attempted
to set the column to something other than `__time`.
(1) and (3) are breaking changes.
Web console changes:
1) Remove "Dimensions" and "Metrics" from the Druid input source.
2) Set timestampSpec to `{"column": "__time", "format": "millis"}` for
compatibility with the new behavior.
Other changes:
1) Add ColumnsFilter, a new class that allows input readers to determine
which columns they need to read. Currently, it's only used by the
DruidInputSource, but it could be used by other columnar input sources
in the future.
2) Add a ColumnsFilter to InputRowSchema.
3) Remove the metric names from InputRowSchema (they were unused).
4) Add InputRowSchemas.fromDataSchema method that computes the proper
ColumnsFilter for given timestamp, dimensions, transform, and metrics.
5) Add "getRequiredColumns" method to TransformSpec to support the above.
* Various fixups.
* Uncomment incorrectly commented lines.
* Move TransformSpecTest to the proper module.
* Add druid.indexer.task.ignoreTimestampSpecForDruidInputSource setting.
* Fix.
* Fix build.
* Checkstyle.
* Misc fixes.
* Fix test.
* Move config.
* Fix imports.
* Fixup.
* Fix ShuffleResourceTest.
* Add import.
* Smarter exclusions.
* Fixes based on tests.
Also, add TIME_COLUMN constant in the web console.
* Adjustments for tests.
* Reorder test data.
* Update docs.
* Update docs to say Druid 0.22.0 instead of 0.21.0.
* Fix test.
* Fix ITAutoCompactionTest.
* Changes from review & from merging.
* add flag to flattenSpec to keep null columns
* remove changes to inputFormat interface
* add comment
* change comment message
* update web console e2e test
* move keepNullColmns to JSONParseSpec
* fix merge conflicts
* fix tests
* set keepNullColumns to false by default
* fix lgtm
* change Boolean to boolean, add keepNullColumns to hash, add tests for keepKeepNullColumns false + true with no nuulul columns
* Add equals verifier tests
* Adding support for autoscaling in GCE
* adding extra google deps also in gce pom
* fix link in doc
* remove unused deps
* adding terms to spelling file
* version in pom 0.17.0-incubating-SNAPSHOT --> 0.18.0-SNAPSHOT
* GCEXyz -> GceXyz in naming for consistency
* add preconditions
* add VisibleForTesting annotation
* typos in comments
* use StringUtils.format instead of String.format
* use custom exception instead of exit
* factorize interval time between retries
* making literal value a constant
* iter all network interfaces
* use provided on google (non api) deps
* adding missing dep
* removing unneded this and use Objects methods instead o 3-way if in hash and comparison
* adding import
* adding retries around getRunningInstances and adding limit for operation end waiting
* refactor GceEnvironmentConfig.hashCode
* 0.18.0-SNAPSHOT -> 0.19.0-SNAPSHOT
* removing unused config
* adding tests to hash and equals
* adding nullable to waitForOperationEnd
* adding testTerminate
* adding unit tests for createComputeService
* increasing retries in unrelated integration-test to prevent sporadic failure (hopefully)
* reverting queryResponseTemplate change
* adding comment for Compute.Builder.build() returning null
* Allow Cloud SegmentKillers to be instantiated without segment bucket or path
This change fixes a bug that was introduced that causes ingestion
to fail if data is ingested from one of the supported cloud storages
(Azure, Google, S3), and the user is using another type of storage
for deep storage. In this case the all segment killer implementations
are instantiated. A change recently made forced a dependency between
the supported cloud storage type SegmentKiller classes and the
deep storage configuration for that storage type being set, which
forced the deep storage bucket and prefix to be non-null. This caused
a NullPointerException to be thrown when instantiating the
SegmentKiller classes during ingestion.
To fix this issue, the respective deep storage segment configs for the
cloud storage types supported in druid are now allowed to have nullable
bucket and prefix configurations
* * Allow google deep storage bucket to be null
* Ability to Delete task logs and segments from Google Storage
* implement ability to delete all tasks logs or all task logs
written before a particular date when written to Google storage
* implement ability to delete all segments from Google deep storage
* * Address review comments
* Skip empty files for local, hdfs, and cloud input sources
* split hint spec doc
* doc for skipping empty files
* fix typo; adjust tests
* unnecessary fluent iterable
* address comments
* fix test
* use the right lists
* fix test
* fix test
* Create splits of multiple files for parallel indexing
* fix wrong import and npe in test
* use the single file split in tests
* rename
* import order
* Remove specific local input source
* Update docs/ingestion/native-batch.md
Co-Authored-By: sthetland <steve.hetland@imply.io>
* Update docs/ingestion/native-batch.md
Co-Authored-By: sthetland <steve.hetland@imply.io>
* doc and error msg
* fix build
* fix a test and address comments
Co-authored-by: sthetland <steve.hetland@imply.io>
* Add Azure config options for segment prefix and max listing length
Added configuration options to allow the user to specify the prefix
within the segment container to store the segment files. Also
added a configuration option to allow the user to specify the
maximum number of input files to stream for each iteration.
* * Fix test failures
* * Address review comments
* * add dependency explicitly to pom
* * update docs
* * Address review comments
* * Address review comments
By default native batch ingestion was only getting a batch of 10
files at a time when used with google cloud. The Default for other
cloud providers is 1024, and should be similar for google cloud.
The low batch size was caused by mistype. This change updates the
batch size to 1024 when using google cloud.
* add prefixes support to google input source, making it symmetrical-ish with s3
* docs
* more better, and tests
* unused
* formatting
* javadoc
* dependencies
* oops
* review comments
* better javadoc
* add s3 input source for native batch ingestion
* add docs
* fixes
* checkstyle
* lazy splits
* fixes and hella tests
* fix it
* re-use better iterator
* use key
* javadoc and checkstyle
* exception
* oops
* refactor to use S3Coords instead of URI
* remove unused code, add retrying stream to handle s3 stream
* remove unused parameter
* update to latest master
* use list of objects instead of object
* serde test
* refactor and such
* now with the ability to compile
* fix signature and javadocs
* fix conflicts yet again, fix S3 uri stuffs
* more tests, enforce uri for bucket
* javadoc
* oops
* abstract class instead of interface
* null or empty
* better error
* Fix the potential race SplittableInputSource.getNumSplits() and SplittableInputSource.createSplits() in TaskMonitor
* Fix docs and javadoc
* Add unit tests for large or small estimated num splits
* add override
* Add FileUtils.createTempDir() and enforce its usage.
The purpose of this is to improve error messages. Previously, the error
message on a nonexistent or unwritable temp directory would be
"Failed to create directory within 10,000 attempts".
* Further updates.
* Another update.
* Remove commons-io from benchmark.
* Fix tests.
* Tidy up lifecycle, query, and ingestion logging.
The goal of this patch is to improve the clarity and usefulness of
Druid's logging for cluster operators. For more information, see
https://twitter.com/cowtowncoder/status/1195469299814555648.
Concretely, this patch does the following:
- Changes a lot of INFO logs to DEBUG, and DEBUG to TRACE, with the
goal of reducing redundancy and improving clarity by avoiding
showing rarely-useful log messages. This includes most "starting"
and "stopping" messages, and most messages related to individual
columns.
- Adds new log4j2 templates that show operators how to enabled DEBUG
logging for certain important packages.
- Eliminate stack traces for query errors, unless log level is DEBUG
or more. This is useful because query errors often indicate user
error rather than system error, but dumping stack trace often gave
operators the impression that there was a system failure.
- Adds task id to Appenderator, AppenderatorDriver thread names. In
the default log4j2 configuration, this will put them in log lines
as well. It's very useful if a user is using the Indexer, where
multiple tasks run in the same JVM.
- More consistent terminology when it comes to "sequences" (sets of
segments that are handed-off together by Kafka ingestion) and
"offsets" (cursors in partitions). These terms had been confused in
some log messages due to the fact that Kinesis calls offsets
"sequence numbers".
- Replaces some ugly toString calls with either the JSONification or
something more operator-accessible (like a URL or segment identifier,
instead of JSON object representing the same).
* Adjustments.
* Adjust integration test.
* Fix dependency analyze warnings
Update the maven dependency plugin to the latest version and fix all
warnings for unused declared and used undeclared dependencies in the
compile scope. Added new travis job to add the check to CI. Also fixed
some source code files to use the correct packages for their imports and
updated druid-forbidden-apis to prevent regressions.
* Address review comments
* Adjust scope for org.glassfish.jaxb:jaxb-runtime
* Fix dependencies for hdfs-storage
* Consolidate netty4 versions
Kashif Faraz
Tejaswini Bandlamudi
Didip Kerabat
Abhishek Agarwal
Jonathan Wei
Karan Kumar
Jihoon Son
Gian Merlino
Clint Wylie
Parag Jain
mcbrewster
Francesco Nidito
zachjsh
jon-wei
Chi Cao Minh
Benedict Jin