Add cache for inverted index query match bitmap to accelerate common query keyword, especially for keyword matching many rows.
Tests result:
- large result: matching 99% out of 247 million rows shows 8x speed up.
- small result: matching 0.1% out of 247 million rows shows 2x speed up.
Issue Number: close#16351
Dynamic schema table is a special type of table, it's schema change with loading procedure.Now we implemented this feature mainly for semi-structure data such as JSON, since JSON is schema self-described we could extract schema info from the original documents and inference the final type infomation.This speical table could reduce manual schema change operation and easily import semi-structure data and extends it's schema automatically.
multiget_data working in bthread and may block the whole worker pthread of BRPC framework and effect other bthreads, so I seperate work task into a seperate task pool.
Currently, for broadcast shuffle, we serialize a block once and then send it by RPC through multiple channel. After this, we will serialize next block in the same memory for consideration of memory reuse. However, since the RPC is asynchronized, maybe the next block serialization will happen before sending the previous block.
So, in this PR, I use a ref count to identify if the serialized block can be reuse in broadcast shuffle.
`CachedRemoteFileReader` has used fixed segment size(file_cache_max_file_segment_size=4M) to cache remote file blocks. However, the column size in a rowgroup/strip maybe smaller than 10K if a parquet/orc file has many columns, resulting in particularly serious read amplification. For example:
Q1 in clickbench: select count(*) from hits
```
- FileCache: 0ns
- IOHitCacheNum: 552
- IOTotalNum: 835
- ReadFromFileCacheBytes: 19.98 MB
- ReadFromWriteCacheBytes: 0.00
- ReadTotalBytes: 29.52 MB
- SkipCacheBytes: 0.00
- WriteInFileCacheBytes: 915.77 MB
- WriteInFileCacheNum: 283
```
Only 30MB of data is needed, but 900MB+ of data is read from hdfs. The query time of Q1(single scan thread) increased from **5.17s** to **24.45s** when enable file cache.
Therefore, this PR introduce dynamic segment size which is based on the `read_size` of the data. In order to prevent too small or too large IO, the segment size is limited in [4096, file_cache_max_file_segment_size].
Q1 in clickbench is **5.66s** when enable file cache. The performance is almost the same as if the cache is disabled, and the data size read from hdfs is reduced to 45MB.
```
- FileCache: 0ns
- IOHitCacheNum: 297
- IOTotalNum: 835
- ReadFromFileCacheBytes: 8.73 MB
- ReadFromWriteCacheBytes: 0.00
- ReadTotalBytes: 29.52 MB
- SkipCacheBytes: 0.00
- WriteInFileCacheBytes: 45.66 MB
- WriteInFileCacheNum: 544
```
## Remaining Problems
Small queries may result in a large number of small files(4KB at least), and the `BE` saves too much meta information of cached segments.
## Fix bug
`FileCachePolicy` in `FileReaderOptions` is a constant reference, but the parameter passed in `FileFactory::create_file_reader` is a temporary variable, resulting in segmentation fault.
MetricRegistry::trigger_all_hooks holds the metrics lock and is stuck in get_je_metrics, to_prometheus is waiting for MetricRegistry::trigger_all_hooks to release the lock, so get_je_metrics is no longer called in MetricRegistry::trigger_all_hooks.
1. When the process memory is insufficient, print the process memory statistics in a more timely and detailed manner.
2. Support regular GC cache, currently only page cache and chunk allocator are included, because many people reported that the memory does not drop after the query ends.
3. Reduce system available memory warning water mark to reduce memory waste
4. Optimize soft mem limit logging
if we set enable_system_metrics to false, we will see be down with following message "enable metric calculator failed,
maybe you set enable_system_metrics to false ", so fix it
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
remove json functions code
remove string functions code
remove math functions code
move MatchPredicate to olap since it is only used in storage predicate process
remove some code in tuple, Tuple structure should be removed in the future.
remove many code in collection value structure, they are useless
1. support row format using codec of jsonb
2. short path optimize for point query
3. support prepared statement for point query
4. support mysql binary format
This PR optimize topn query like `SELECT * FROM tableX ORDER BY columnA ASC/DESC LIMIT N`.
TopN is is compose of SortNode and ScanNode, when user table is wide like 100+ columns the order by clause is just a few columns.But ScanNode need to scan all data from storage engine even if the limit is very small.This may lead to lots of read amplification.So In this PR I devide TopN query into two phase:
1. The first phase we just need to read `columnA`'s data from storage engine along with an extra RowId column called `__DORIS_ROWID_COL__`.The other columns are pruned from ScanNode.
2. The second phase I put it in the ExchangeNode beacuase it's the central node for topn nodes in the cluster.The ExchangeNode will spawn a RPC to other nodes using the RowIds(sorted and limited from SortNode) read from the first phase and read row by row from storage engine.
After the second phase read, Block will contain all the data needed for the query
Step3 of DSIP-023: Add inverted index for full text search
implementation of inverted index writer for numeric types, using bkd index
dependency pr: #14207#15807#15821
The main purpose of this pr is to import `fileCache` for lakehouse reading remote files.
Use the local disk as the cache for reading remote file, so the next time this file is read,
the data can be obtained directly from the local disk.
In addition, this pr includes a few other minor changes
Import File Cache:
1. The imported `fileCache` is called `block_file_cache`, which uses lru replacement policy.
2. Implement a new FileRereader `CachedRemoteFilereader`, so that the logic of `file cache` is hidden under `CachedRemoteFilereader`.
Other changes:
1. Add a new interface `fs()` for `FileReader`.
2. `IOContext` adds some statistical information to count the situation of `FileCache`
Co-authored-by: Lightman <31928846+Lchangliang@users.noreply.github.com>
* [feature-wip](inverted index)inverted index api: reader
* [feature-wip](inverted index) Fulltext query syntax with MATCH/MATCH_ALL/MATCH_ALL
* [feature-wip](inverted index) Adapt to index meta
* [enhance] add more metrics
* [enhance] add fulltext match query check for column type and index parser
* [feature-wip](inverted index) Support apply inverted index in compound predicate which except leaf node of and node
Current bitmap index only can apply pushed down predicates which in AND conditions. When predicates in OR conditions and other complex compound conditions, it will not be pushed down to the storage layer, this leads to read more data.
Based on that situation, this pr will do:
1. this pr in order to support bitmap index apply compound predicates, query sql like:
select * from tb where a > 'hello' or b < 100;
select * from tb where a > 'hello' or b < 100 or c > 'ok';
select * from tb where (a > 'hello' or b <100) and (a < 'world' or b > 200);
select * from tb where (not a> 'hello') or b < 100;
...
above sql，column a and b and c has created bitmap_index.
2. this optimization can reduce reading data by index
3. set config enable_index_apply_compound_predicates to use this optimization
This PR implement the new bloom filter index: NGram bloom filter index, which was proposed in #10733.
The new index can improve the like query performance greatly, from our some test case , can get order of magnitude improve.
For how to use it you can check the docs in this PR, and the index based on the ```enable_function_pushdown```,
you need set it to ```true```, to make the index work for like query.
Add a new config "jdbc_drivers_dir" for both FE and BE.
User can put jdbc drivers' jar file in this dir, and only specify file name in "driver_url" properties
when creating jdbc resource.
And Doris will find jar files in this dir.
Also modify the logic so that when the jdbc resource is modified, the corresponding jdbc table
will get the latest properties.
