1. Add hdfs file handle cache for hdfs file reader
Copied from Impala, `https://github.com/apache/impala/blob/master/be/src/util/lru-multi-cache.h`. (Thanks for the Impala team)
This is a lru cache that can store multi entries with same key.
The key is build with {file name + modification time}
The value is the hdfsFile pointer that point to a certain hdfs file.
This cache is to avoid reopen same hdfs file mutli time, which can save
query time.
Add a BE config `max_hdfs_file_handle_cache_num` to limit the max number
of file handle cache, default is 20000.
2. Add file meta cache
The file meta cache is a lru cache. the key is {file name + modification time},
the value is the parsed file meta info of the certain file, which can save
the time of re-parsing file meta everytime.
Currently, it is only used for caching parquet file footer.
The test show that is cache is hit, the `FileOpenTime` and `ParseFooterTime` is reduce to almost 0
in query profile, which can save time when there are lots of files to read.
For routine load (kafka load), user can produce all data for different
table into single topic and doris will dispatch them into corresponding
table.
Signed-off-by: freemandealer <freeman.zhang1992@gmail.com>
Optimize the strategy of merging small IO to prevent severe read amplification, and turn off merged IO when file cache enabled.
Adjustable parameters:
```
// the max amplified read ratio when merging small IO
max_amplified_read_ratio=0.8
// the min segment size
file_cache_min_file_segment_size = 1048576
```
Test on SSB 100g:
select lo_suppkey, count(distinct lo_linenumber) from lineorder group by lo_suppkey;
exec time: 4.388s
create materialized view:
create materialized view customer_uv as select lo_suppkey, bitmap_union(to_bitmap(lo_linenumber)) from lineorder group by lo_suppkey;
select lo_suppkey, count(distinct lo_linenumber) from lineorder group by lo_suppkey;
exec time: 12.908s
test with the patch, exec time: 5.790s
Currently, compaction is executed separately for each backend, and the reconstruction of the index during compaction leads to high CPU usage. To address this, we are introducing single replica compaction, where a specific primary replica is selected to perform compaction, and the remaining replicas fetch the compaction results from the primary replica.
The Backend (BE) requests replica information for all peers corresponding to a tablet from the Frontend (FE). This information includes the host where the replica is located and the replica_id. By calculating hash(replica_id), the replica with the smallest hash value is responsible for executing compaction, while the remaining replicas are responsible for fetching the compaction results from this replica.
The compaction task producer thread, before submitting a compaction task, checks whether the local replica should fetch from its peer. If it should, the task is then submitted to the single replica compaction thread pool.
When performing single replica compaction, the process begins by requesting rowset versions from the target replica. These rowset_versions are then compared with the local rowset versions. The first version that can be fetched is selected.
* [Improve](performance) introduce SchemaCache to cache TabletSchame & Schema
1. When the system is under high-concurrency load with wide table point queries, the frequent memory allocation and deallocation of Schema become evident system bottlenecks. Additionally, the initialization of TabletSchema and Schema also becomes a CPU hotspot.Therefore, the introduction of a SchemaCache is implemented to cache these resources for reuse.
2. Make some variables wrapped with std::unique<unique_ptr>
Performance:
| 状态 | QPS | 平均响应时间 (avg) | P99 响应时间 |
|------------------|-----|------------------|-------------|
| 开启 SchemaCache | 501 | 20ms | 34ms |
| 关闭 SchemaCache | 321 | 31ms | 61ms |
* handle schema change with schema version
* remove useless header
* rebase
Support to gc inverted index cache when there is not enough memory.
previous problem： The inverted index cache (InvertedIndexSearcherCache and InvertedIndexQueryCache) may use 20% memory which can't be released.
In mow, primary key cache have a big impact on load performance, so we add a new cache type to seperate
it from page cache to make it more flexible in some cases
fix mem_limit default value
memory_gc_sleep_time_s to memory_gc_sleep_time_ms
LoadChannelMgr::_handle_mem_exceed_limit process_mem_limit to process soft mem limit
fix query mem tracker print
For performance issue, we would specify rowset included by cold heat separation table to use file block cache no matter what config user has set.
I've tested the config using cold_heat_seperation_case_p2 and it works well.
Supplement the documentation of be-clion-dev, avoid the problem of undefined DORIS_JAVA_HOME and inability to find jni.h when using clion development without directly compiling through build.sh
Complete the classification of header files in pch.h and introduce some header files that are not frequently modified in doris.
Separate the declaration and definition in common/config.h. If you need to modify the default configuration now, please modify it in common/config.cpp.
gen_cpp/version.h is regenerated every time it is recompiled, which may cause PCH to fail, so now you need to get the version information indirectly rather than directly.
