Kinesis ingestion requires all shards to have at least 1 record at the required position in druid.
Even if this is satisified initially, resharding the stream can lead to empty intermediate shards. A significant delay in writing to newly created shards was also problematic.
Kinesis shard sequence numbers are big integers. Introduce two more custom sequence tokens UNREAD_TRIM_HORIZON and UNREAD_LATEST to indicate that a shard has not been read from and that it needs to be read from the start or the end respectively.
These values can be used to avoid the need to read at least one record to obtain a sequence number for ingesting a newly discovered shard.
If a record cannot be obtained immediately, use a marker to obtain the relevant shardIterator and use this shardIterator to obtain a valid sequence number. As long as a valid sequence number is not obtained, continue storing the token as the offset.
These tokens (UNREAD_TRIM_HORIZON and UNREAD_LATEST) are logically ordered to be earlier than any valid sequence number.
However, the ordering requires a few subtle changes to the existing mechanism for record sequence validation:
The sequence availability check ensures that the current offset is before the earliest available sequence in the shard. However, current token being an UNREAD token indicates that any sequence number in the shard is valid (despite the ordering)
Kinesis sequence numbers are inclusive i.e if current sequence == end sequence, there are more records left to read.
However, the equality check is exclusive when dealing with UNREAD tokens.
* Add check for eternity time segment to SqlSegmentsMetadataQuery
* Add check for half eternities
* Add multiple segments test
* Add failing test to document known issue
* Frame processing and channels.
Follow-up to #12745. This patch adds three new concepts:
1) Frame channels are interfaces for doing nonblocking reads and writes
of frames.
2) Frame processors are interfaces for doing nonblocking processing of
frames received from input channels and sent to output channels.
3) Cluster-by keys, which can be used for sorting or partitioning.
The patch also adds SuperSorter, a user of these concepts, both to
illustrate how they are used, and also because it is going to be useful
in future work.
Central classes:
- ReadableFrameChannel. Implementations include
BlockingQueueFrameChannel (in-memory channel that implements both interfaces),
ReadableFileFrameChannel (file-based channel),
ReadableByteChunksFrameChannel (byte-stream-based channel), and others.
- WritableFrameChannel. Implementations include BlockingQueueFrameChannel
and WritableStreamFrameChannel (byte-stream-based channel).
- ClusterBy, a sorting or partitioning key.
- FrameProcessor, nonblocking processor of frames. Implementations include
FrameChannelBatcher, FrameChannelMerger, and FrameChannelMuxer.
- FrameProcessorExecutor, an executor service that runs FrameProcessors.
- SuperSorter, a class that uses frame channels and processors to
do parallel external merge sort of any amount of data (as long as there
is enough disk space).
* Additional tests, fixes.
* Changes from review.
* Better implementation for ReadableInputStreamFrameChannel.
* Rename getFrameFileReference -> newFrameFileReference.
* Add InterruptedException to runIncrementally; add more tests.
* Cancellation adjustments.
* Review adjustments.
* Refactor BlockingQueueFrameChannel, rename doneReading and doneWriting to close.
* Additional changes from review.
* Additional changes.
* Fix test.
* Adjustments.
* Adjustments.
* Refactor Guice initialization
Builders for various module collections
Revise the extensions loader
Injector builders for server startup
Move Hadoop init to indexer
Clean up server node role filtering
Calcite test injector builder
* Revisions from review comments
* Build fixes
* Revisions from review comments
* Improved Java 17 support and Java runtime docs.
1) Add a "Java runtime" doc page with information about supported
Java versions, garbage collection, and strong encapsulation..
2) Update asm and equalsverifier to versions that support Java 17.
3) Add additional "--add-opens" lines to surefire configuration, so
tests can pass successfully under Java 17.
4) Switch openjdk15 tests to openjdk17.
5) Update FrameFile to specifically mention Java runtime incompatibility
as the cause of not being able to use Memory.map.
6) Update SegmentLoadDropHandler to log an error for Errors too, not
just Exceptions. This is important because an IllegalAccessError is
encountered when the correct "--add-opens" line is not provided,
which would otherwise be silently ignored.
7) Update example configs to use druid.indexer.runner.javaOptsArray
instead of druid.indexer.runner.javaOpts. (The latter is deprecated.)
* Adjustments.
* Use run-java in more places.
* Add run-java.
* Update .gitignore.
* Exclude hadoop-client-api.
Brought in when building on Java 17.
* Swap one more usage of java.
* Fix the run-java script.
* Fix flag.
* Include link to Temurin.
* Spelling.
* Update examples/bin/run-java
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
* Use nonzero default value of maxQueuedBytes.
The purpose of this parameter is to prevent the Broker from running out
of memory. The prior default is unlimited; this patch changes it to a
relatively conservative 25MB.
This may be too low for larger clusters. The risk is that throughput
can decrease for queries with large resultsets or large amounts of intermediate
data. However, I think this is better than the risk of the prior default, which
is that these queries can cause the Broker to go OOM.
* Alter calculation.
* Python 3 support for post-index-task.
Useful when running on macOS or any other system that
doesn't have Python 2.
* Encode JSON returned by read_task_file.
* Adjust.
* Skip needless loads.
* Add a decode.
* Additional decodes needed.
Historicals and middle managers crash with an `UnknownHostException` on trying
to load `druid-parquet-extensions` with an ephemeral Hadoop cluster. This happens
because the `fs.defaultFS` URI value cannot be resolved at start up time as the
hadoop cluster may not exist at startup time.
This commit fixes the error by performing initialization of the filesystem in
`ParquetInputFormat.createReader()` whenever a new reader is requested.
add NumericRangeIndex interface and BoundFilter support
changes:
* NumericRangeIndex interface, like LexicographicalRangeIndex but for numbers
* BoundFilter now uses NumericRangeIndex if comparator is numeric and there is no extractionFn
* NestedFieldLiteralColumnIndexSupplier.java now supports supplying NumericRangeIndex for single typed numeric nested literal columns
* better faster stronger and (ever so slightly) more understandable
* more tests, fix bug
* fix style
* Druid planner now makes only one pass through Calcite planner
Resolves the issue that required two parse/plan cycles: one
for validate, another for plan. Creates a clone of the Calcite
planner and validator to resolve the conflict that prevented
the merger.
* Fixes for the Avatica JDBC driver
Correctly implement regular and prepared statements
Correctly implement result sets
Fix race condition with contexts
Clarify when parameters are used
Prepare for single-pass through the planner
* Addressed review comments
* Addressed review comment
Some queries like `REPLACE INTO ... SELECT TIME_PARSE("__time") AS __time FROM ...`
fail at the Calcite layer because any column with name `__time` is considered to be of
type `SqlTypeName.TIMESTAMP`.
Changes:
- Modify `RowSignatures.toRelDataType()` so that the type of `__time` column
is determined by the RowSignature's type.
Sysmonitor stats (mem, fs, disk, net, cpu, swap, sys, tcp) are reported by all Druid processes, including Peons that are ephemeral in nature. Since Peons always run on the same host as the MiddleManager that spawned them and is unlikely to change, the SyMonitor metrics emitted by Peon are merely duplicates. This is often not a problem except when machines are super-beefy. Imagine a 64-core machine and 32 workers running on this machine. now you will have each Peon reporting metrics for each core. that's an increase of (32 * 64)x in the number of metrics. This leads to a metric explosion.
This PR updates MetricsModule to check node role running while registering SysMonitor and not to load any existing SysMonitor$Stats.
This commit suppresses the following CVEs:
- CVE-2021-43138: false alarm for async-http-client
- CVE-2021-34538: applicable to Hive server
- CVE-2020-25638: requires hibernate update, which causes Hadoop ingestion failure
- CVE-2021-27568: false alarm for accessors-smart which is a dependency of json-smart (already suppressed)
* Add clarification for combining input source
* Update inputFormat note
* Update maxNumConcurrentSubTasks note
* Fix broken link
* Update docs/ingestion/native-batch-input-source.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Few indexing tasks register RealtimeMetricsMonitor or TaskRealtimeMetricsMonitor with the process’s MonitorScheduler when they start. These monitors never unregister themselves (they always return true, they'd need to return false to unregister). Each of these monitors emits a set of metrics once every druid.monitoring.emissionPeriod.
As a result, after executing several tasks for a while, Indexer emits metrics of these tasks even after they're long gone.
Proposed Solution
Since one should be able to obtain the last round of ingestion metrics after the task unregisters the monitor, introducing lastRoundMetricsToBePushed variable to keep track of the same and overriding the AbstractMonitor.monitor method in RealtimeMetricsMonitor, TaskRealtimeMetricsMonitor to implement the new logic.
Karan Kumar
Jonathan Wei
Herb Brewer
AmatyaAvadhanula
Adarsh Sanjeev
Gian Merlino
Katya Macedo
Clint Wylie
Paul Rogers
刘小辉
Peter Marshall
Rohan Garg
Tejaswini Bandlamudi
317brian
Charles Smith
Maytas Monsereenusorn
Atul Mohan
Jacques Arnoux
Laksh Singla
PJ Fanning
Victoria Lim
Kashif Faraz
Jianhuan Liu
Vadim Ogievetsky