Remove the default constructor for UniqueID
Add a gen_uid method in UniqueId. If need to generate a new uid, users should call this api explicitly.
Reuse boost random generator not generate a new one every time.
Commit kafka offset in routine load
Kafka will decide whether to delete data based on whether all consumer group is commit offset or not. If there is no commit offset, the kafka server disk may be full
1. Support specifying label to Insert Into stmt.
INSERT INTO tbl1 WITH LABEL label1 ...;
2. Return job' state corresponding to the existing label in result of stream load.
...
"Status": "Label Already Exists",
"ExistingJobStatus": "FINISHED"
...
3. Return the recent 2000 transactions in SHOW PROC '/transactions'
Mini load is now using stream load framework. But we should keep the
mini load return behavior and result json format be same as old.
So PUBLISH_TIMEOUT error should be treated as OK in mini load.
Also add 2 counters for OlapTableSink profile:
SerializeBatchTime: time of serializing all row batch.
WaitInFlightPacketTime: time of waiting last send packet
Mini load timeout needs to be added in plan options.
The timeout property has been added in request of process put.
Otherwise, the timeout of mini load is useless.
Add log of label, txn and query id in mini load
* This commit has brought contribution to streaming mini load
The operation of streaming mini load is sames as previous. Also, user can check the load by frontend.
The difference is that streaming mini load finish the task before reply of REST API while the non-streaming only register a load.
* When updating doris
Updating fe or be firstly are also supported. After fe and be are updated, the streaming mini load will take effect.
* For multi mini load
The non-streaming mini load still has been used by multi mini load. The behavior of multi mini load has not been changed.
* Add a interface named isSupportedFunction
This function is used to protect the correctness of new feature which consists of be and fe during updaing.
* Enhence the usabilities
1. Add metrics to monitor transactions and steaming load process in BE.
2. Modify BE config 'result_buffer_cancelled_interval_time' to 300s.
3. Modify FE config 'enable_metric_calculator' to true.
4. Add more log for tracing broker load process.
5. Modify the query report process, to cancel query immediately if some instance failed.
* Fix bugs
1. Avoid NullPointer when enabling colocation join with broker load
2. Return immediately when pull load task coordinator execution failed
1. Use a data consumer group to share a single stream load pipe with multi data consumers. This will increase the consuming speed of Kafka messages, as well as reducing the task number of routine
load job.
Test results：
* 1 consumer, 1 partitions:
consume time: 4.469s, rows: 990140, bytes: 128737139. 221557 rows/s, 28M/s
* 1 consumer, 3 partitions:
consume time: 12.765s, rows: 2000143, bytes: 258631271. 156689 rows/s, 20M/s
blocking get time(us): 12268241, blocking put time(us): 1886431
* 3 consumers, 3 partitions:
consume time(all 3): 6.095s, rows: 2000503, bytes: 258631576. 328220 rows/s, 42M/s
blocking get time(us): 1041639, blocking put time(us): 10356581
The next 2 cases show that we can achieve higher speed by adding more consumers. But the bottle neck transfers from Kafka consumer to Doris ingestion, so 3 consumers in a group is enough.
I also add a Backend config `max_consumer_num_per_group` to change the number of consumers in a data consumer group, and default value is 3.
In my test(1 Backend, 2 tablets, 1 replicas), 1 routine load task can achieve 10M/s, which is same as raw stream load.
2. Add OFFSET_BEGINNING and OFFSET_END support for Kafka routine load
1. stream load executor will abort txn when no correct data in task
2. change txn label to DebugUtil.print(UUID) which is same as task id printed by be
3. change print uuid to hi-lo
1. init cmt offset in stream load context
2. init default max error num = 5000 rows / per 10000 rows
3. add log builder for routine load job and task
4. clone plan fragment param for every task
5. be does not throw too many filter rows while the init max error ratio is 1
1. Check if properties is null before check routine load properties
2. Change transactionStateChange reason to string
3. calculate current num by beId
4. Add kafka offset properties
5. Prefer to use previous be id
6. Add before commit listener of txn: if txn is committed after task is aborted, commit will be aborted
7. queryId of stream load plan = taskId
