Problem
Currently, the delta input source only supports reading from the latest snapshot of the given Delta Lake table. This is a known documented limitation.
Description
Add support for reading Delta snapshot. By default, the Druid-Delta connector reads the latest snapshot of the Delta table in order to preserve compatibility. Users can specify a snapshotVersion to ingest change data events from Delta tables into Druid.
In the future, we can also add support for time-based snapshot reads. The Delta API to read time-based snapshots is not clear currently.
changes:
* Adds new `CompressedComplexColumn`, `CompressedComplexColumnSerializer`, `CompressedComplexColumnSupplier` based on `CompressedVariableSizedBlobColumn` used by JSON columns
* Adds `IndexSpec.complexMetricCompression` which can be used to specify compression for the generic compressed complex column. Defaults to uncompressed because compressed columns are not backwards compatible.
* Adds new definition of `ComplexMetricSerde.getSerializer` which accepts an `IndexSpec` argument when creating a serializer. The old signature has been marked `@Deprecated` and has a default implementation that returns `null`, but it will be used by the default implementation of the new version if it is implemented to return a non-null value. The default implementation of the new method will use a `CompressedComplexColumnSerializer` if `IndexSpec.complexMetricCompression` is not null/none/uncompressed, or will use `LargeColumnSupportedComplexColumnSerializer` otherwise.
* Removed all duplicate generic implementations of `ComplexMetricSerde.getSerializer` and `ComplexMetricSerde.deserializeColumn` into default implementations `ComplexMetricSerde` instead of being copied all over the place. The default implementation of `deserializeColumn` will check if the first byte indicates that the new compression was used, otherwise will use the `GenericIndexed` based supplier.
* Complex columns with custom serializers/deserializers are unaffected and may continue doing whatever it is they do, either with specialized compression or whatever else, this new stuff is just to provide generic implementations built around `ObjectStrategy`.
* add ObjectStrategy.readRetainsBufferReference so CompressedComplexColumn only copies on read if required
* add copyValueOnRead flag down to CompressedBlockReader to avoid buffer duplicate if the value needs copied anyway
Design:
The loading rate is computed as a moving average of at least the last 10 GiB of successful segment loads.
To account for multiple loading threads on a server, we use the concept of a batch to track load times.
A batch is a set of segments added by the coordinator to the load queue of a server in one go.
Computation:
batchDurationMillis = t(load queue becomes empty) - t(first load request in batch is sent to server)
batchBytes = total bytes successfully loaded in batch
avg loading rate in batch (kbps) = (8 * batchBytes) / batchDurationMillis
overall avg loading rate (kbps) = (8 * sumOverWindow(batchBytes)) / sumOverWindow(batchDurationMillis)
Changes:
- Add `LoadingRateTracker` which computes a moving average load rate based on
the last few GBs of successful segment loads.
- Emit metric `segment/loading/rateKbps` from the Coordinator. In the future, we may
also consider emitting this metric from the historicals themselves.
- Add `expectedLoadTimeMillis` to response of API `/druid/coordinator/v1/loadQueue?simple`
* batch 03 - trig functions
* Apply suggestions from code review
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* applying suggestions and corrections
---------
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
changes:
* removed `Firehose` and `FirehoseFactory` and remaining implementations which were mostly no longer used after #16602
* Moved `IngestSegmentFirehose` which was still used internally by Hadoop ingestion to `DatasourceRecordReader.SegmentReader`
* Rename `SQLFirehoseFactoryDatabaseConnector` to `SQLInputSourceDatabaseConnector` and similar renames for sub-classes
* Moved anything remaining in a 'firehose' package somewhere else
* Clean up docs on firehose stuff
* updating first batch of numeric functions
* First batch of functions
* addressing first few comments
* alphabetize list
* draft with suggestions applied
* minor discrepency expr -> <NUMERIC>
* changed raises to calculates
* Update docs/querying/sql-functions.md
* switch to underscore
* changed to exp(1) to match slack message
* adding html text for trademark symbol to .spelling
* fixed discrepancy between description and example
---------
Co-authored-by: Benedict Jin <asdf2014@apache.org>
index_realtime tasks were removed from the documentation in #13107. Even
at that time, they weren't really documented per se— just mentioned. They
existed solely to support Tranquility, which is an obsolete ingestion
method that predates migration of Druid to ASF and is no longer being
maintained. Tranquility docs were also de-linked from the sidebars and
the other doc pages in #11134. Only a stub remains, so people with
links to the page can see that it's no longer recommended.
index_realtime_appenderator tasks existed in the code base, but were
never documented, nor as far as I am aware were they used for any purpose.
This patch removes both task types completely, as well as removes all
supporting code that was otherwise unused. It also updates the stub
doc for Tranquility to be firmer that it is not compatible. (Previously,
the stub doc said it wasn't recommended, and pointed out that it is
built against an ancient 0.9.2 version of Druid.)
ITUnionQueryTest has been migrated to the new integration tests framework and updated to use Kafka ingestion.
Co-authored-by: Gian Merlino <gianmerlino@gmail.com>
Thanks for your contribution @amit-git-account
* Added new use cases and description of the use case - 5/14/24
The use case listing is not changed in a long time. While speaking with users, I came across several other use cases not listed here in the index. So I added new use cases and also added description against the use cases.
* Apply suggestions from code review
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
* update spelling file
* Update docs/design/index.md
---------
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
Co-authored-by: Benedict Jin <asdf2014@apache.org>
Issue: #14989
The initial step in optimizing segment metadata was to centralize the construction of datasource schema in the Coordinator (#14985). Thereafter, we addressed the problem of publishing schema for realtime segments (#15475). Subsequently, our goal is to eliminate the requirement for regularly executing queries to obtain segment schema information.
This is the final change which involves publishing segment schema for finalized segments from task and periodically polling them in the Coordinator.
Support for exporting msq results to gcs bucket. This is essentially copying the logic of s3 export for gs, originally done by @adarshsanjeev in this PR - #15689
This PR creates an interface for ImmutableRTree and moved the existing implementation to new class which represent 32 bit implementation (stores coordinate as floats). This PR makes the ImmutableRTree extendable to create higher precision implementation as well (64 bit).
In all spatial bound filters, we accept float as input which might not be accurate in the case of high precision implementation of ImmutableRTree. This PR changed the bound filters to accepts the query bounds as double instead of float and it is backward compatible change as it compares double to existing float values in RTree. Previously it was comparing input float to RTree floats which can cause precision loss, now it is little better as it compares double to float which is still not 100% accurate.
There are no changes in the way that we query spatial dimension today except input bound parsing. There is little improvement in string filter predicate which now parse double strings instead of float and compares double to double which is 100% accurate but string predicate is only called when we dont have spatial index.
With allowing the interface to extend ImmutableRTree, we allow to create high precision (HP) implementation and defines new search strategies to perform HP search Iterable<ImmutableBitmap> search(ImmutableDoubleNode node, Bound bound);
With possible HP implementations, Radius bound filter can not really focus on accuracy, it is calculating Euclidean distance in comparing. As EARTH 🌍 is round and not flat, Euclidean distances are not accurate in geo system. This PR adds new param called 'radiusUnit' which allows you to specify units like meters, km, miles etc. It uses https://en.wikipedia.org/wiki/Haversine_formula to check if given geo point falls inside circle or not. Added a test that generates set of points inside and outside in RadiusBoundTest.
Running npm run build, you should no longer get the message:
[ERROR] Invalid docusaurus-theme-mermaid version 2.4.3.
All official @docusaurus/* packages should have the exact same version as @docusaurus/core (2.4.1).
Maybe you want to check, or regenerate your yarn.lock or package-lock.json file?
yarn build didn't have the same issue, and yarn install did not generate a new yarn.lock
* Add support for AzureDNSZone enabled storage accounts used for deep storage
Added a new config to AzureAccountConfig
`storageAccountEndpointSuffix`
which allows the user to specify a storage account endpoint suffix where the underlying
storage account is enabled for AzureDNSZone. The previous config `endpointSuffix`, did not allow
support for such accounts. The previous config has been deprecated in favor of this new config. Also
fixed an issue where `managedIdentityClientId` was not being set properly
* * address review comments
* * add back azure government link and docs
Merging the work so far. @ektravel , @vogievetsky if there are additional improvements, let's track them & make another pr.
* Refactor streaming ingestion docs
* Update property definition
* Update after review
* Update known issues
* Move kinesis and kafka topics to ingestion, add redirects
* Saving changes
* Saving
* Add input format text
* Update after review
* Minor text edit
* Update example syntax
* Revert back to colon
* Fix merge conflicts
* Fix broken links
* Fix spelling error
* something
* test commit
* compilation fix
* more compilation fixes (fixme placeholders)
* Comment out druid-kereberos build since it conflicts with newly added transitive deps from delta-lake
Will need to sort out the dependencies later.
* checkpoint
* remove snapshot schema since we can get schema from the row
* iterator bug fix
* json json json
* sampler flow
* empty impls for read(InputStats) and sample()
* conversion?
* conversion, without timestamp
* Web console changes to show Delta Lake
* Asset bug fix and tile load
* Add missing pieces to input source info, etc.
* fix stuff
* Use a different delta lake asset
* Delta lake extension dependencies
* Cleanup
* Add InputSource, module init and helper code to process delta files.
* Test init
* Checkpoint changes
* Test resources and updates
* some fixes
* move to the correct package
* More tests
* Test cleanup
* TODOs
* Test updates
* requirements and javadocs
* Adjust dependencies
* Update readme
* Bump up version
* fixup typo in deps
* forbidden api and checkstyle checks
* Trim down dependencies
* new lines
* Fixup Intellij inspections.
* Add equals() and hashCode()
* chain splits, intellij inspections
* review comments and todo placeholder
* fix up some docs
* null table path and test dependencies. Fixup broken link.
* run prettify
* Different test; fixes
* Upgrade pyspark and delta-spark to latest (3.5.0 and 3.0.0) and regenerate tests
* yank the old test resource.
* add a couple of sad path tests
* Updates to readme based on latest.
* Version support
* Extract Delta DateTime converstions to DeltaTimeUtils class and add test
* More comprehensive split tests.
* Some test renames.
* Cleanup and update instructions.
* add pruneSchema() optimization for table scans.
* Oops, missed the parquet files.
* Update default table and rename schema constants.
* Test setup and misc changes.
* Add class loader logic as the context class loader is unaware about extension classes
* change some table client creation logic.
* Add hadoop-aws, hadoop-common and related exclusions.
* Remove org.apache.hadoop:hadoop-common
* Apply suggestions from code review
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Add entry to .spelling to fix docs static check
---------
Co-authored-by: abhishekagarwal87 <1477457+abhishekagarwal87@users.noreply.github.com>
Co-authored-by: Laksh Singla <lakshsingla@gmail.com>
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Possibly stabilize intellij-inspections
* remove `integration-tests-ex/cases` from excluded projects from initial build
* enable ErrorProne's `CheckedExceptionNotThrown` to get earlier errors than intellij-inspections
* fix ddsketch pom.xml
* fix spellcheck
* New: Add DDSketch-Druid extension
- Based off of http://www.vldb.org/pvldb/vol12/p2195-masson.pdf and uses
the corresponding https://github.com/DataDog/sketches-java library
- contains tests for post building and using aggregation/post
aggregation.
- New aggregator: `ddSketch`
- New post aggregators: `quantileFromDDSketch` and
`quantilesFromDDSketch`
* Fixing easy CodeQL warnings/errors
* Fixing docs, and dependencies
Also moved aggregator ids to AggregatorUtil and PostAggregatorIds
* Adding more Docs and better null/empty handling for aggregators
* Fixing docs, and pom version
* DDSketch documentation format and wording
### Description
Our Kinesis consumer works by using the [GetRecords API](https://docs.aws.amazon.com/kinesis/latest/APIReference/API_GetRecords.html) in some number of `fetchThreads`, each fetching some number of records (`recordsPerFetch`) and each inserting into a shared buffer that can hold a `recordBufferSize` number of records. The logic is described in our documentation at: https://druid.apache.org/docs/27.0.0/development/extensions-core/kinesis-ingestion/#determine-fetch-settings
There is a problem with the logic that this pr fixes: the memory limits rely on a hard-coded “estimated record size” that is `10 KB` if `deaggregate: false` and `1 MB` if `deaggregate: true`. There have been cases where a supervisor had `deaggregate: true` set even though it wasn’t needed, leading to under-utilization of memory and poor ingestion performance.
Users don’t always know if their records are aggregated or not. Also, even if they could figure it out, it’s better to not have to. So we’d like to eliminate the `deaggregate` parameter, which means we need to do memory management more adaptively based on the actual record sizes.
We take advantage of the fact that GetRecords doesn’t return more than 10MB (https://docs.aws.amazon.com/streams/latest/dev/service-sizes-and-limits.html ):
This pr:
eliminates `recordsPerFetch`, always use the max limit of 10000 records (the default limit if not set)
eliminate `deaggregate`, always have it true
cap `fetchThreads` to ensure that if each fetch returns the max (`10MB`) then we don't exceed our budget (`100MB` or `5% of heap`). In practice this means `fetchThreads` will never be more than `10`. Tasks usually don't have that many processors available to them anyway, so in practice I don't think this will change the number of threads for too many deployments
add `recordBufferSizeBytes` as a bytes-based limit rather than records-based limit for the shared queue. We do know the byte size of kinesis records by at this point. Default should be `100MB` or `10% of heap`, whichever is smaller.
add `maxBytesPerPoll` as a bytes-based limit for how much data we poll from shared buffer at a time. Default is `1000000` bytes.
deprecate `recordBufferSize`, use `recordBufferSizeBytes` instead. Warning is logged if `recordBufferSize` is specified
deprecate `maxRecordsPerPoll`, use `maxBytesPerPoll` instead. Warning is logged if maxRecordsPerPoll` is specified
Fixed issue that when the record buffer is full, the fetchRecords logic throws away the rest of the GetRecords result after `recordBufferOfferTimeout` and starts a new shard iterator. This seems excessively churny. Instead, wait an unbounded amount of time for queue to stop being full. If the queue remains full, we’ll end up right back waiting for it after the restarted fetch.
There was also a call to `newQ::offer` without check in `filterBufferAndResetBackgroundFetch`, which seemed like it could cause data loss. Now checking return value here, and failing if false.
### Release Note
Kinesis ingestion memory tuning config has been greatly simplified, and a more adaptive approach is now taken for the configuration. Here is a summary of the changes made:
eliminates `recordsPerFetch`, always use the max limit of 10000 records (the default limit if not set)
eliminate `deaggregate`, always have it true
cap `fetchThreads` to ensure that if each fetch returns the max (`10MB`) then we don't exceed our budget (`100MB` or `5% of heap`). In practice this means `fetchThreads` will never be more than `10`. Tasks usually don't have that many processors available to them anyway, so in practice I don't think this will change the number of threads for too many deployments
add `recordBufferSizeBytes` as a bytes-based limit rather than records-based limit for the shared queue. We do know the byte size of kinesis records by at this point. Default should be `100MB` or `10% of heap`, whichever is smaller.
add `maxBytesPerPoll` as a bytes-based limit for how much data we poll from shared buffer at a time. Default is `1000000` bytes.
deprecate `recordBufferSize`, use `recordBufferSizeBytes` instead. Warning is logged if `recordBufferSize` is specified
deprecate `maxRecordsPerPoll`, use `maxBytesPerPoll` instead. Warning is logged if maxRecordsPerPoll` is specified
* Undocument unused segments retrieval API.
* Mark API deprecated and unstable. Note that it'll be removed.
* Cleanup .spelling entries
* Remove the Unstable annotation
Abhishek Radhakrishnan
Clint Wylie
317brian
Edgar Melendrez
Hugh Evans
Kashif Faraz
Charles Smith
Andreas Maechler
Victoria Lim
Amit
Katya Macedo
Jill Osborne
Adithya Chakilam
Rishabh Singh
Sree Charan Manamala
Pranav
Parag Jain
zachjsh
Jamie
Atul Mohan
George Shiqi Wu
Zoltan Haindrich
Hiroshi Fukada