Mainly contains the following modifications:
1. Use `std::unique_ptr` to replace some naked pointers
2. Modify some methods from member-method to local-static-function
3. Modify some methods do not need to be public to private
4. Some formatting changes: such as wrapping lines that are too long
5. Remove some useless variables
6. Add or modify some comments for easier understanding
No functional changes in this patch.
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
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
