1. Fixed a problem with histogram statistics collection parameters.
2. Solved the problem that it takes a long time to collect histogram statistics.
TODO: Optimize histogram statistics sampling method and make the sampling parameters effective.
The problem is that the histogram function works as expected in the single-node test, but doesn't work in the multi-node test. In addition, the performance of the current support sampling to collect histogram is low, resulting in a large time consumption when collecting histogram information.
Fixed the parameter issue and temporarily removed support for sampling to speed up the collection of histogram statistics.
Will next support sampling to collect histogram information.
This commit support:
1、Insert + select for struct/map type
2、Json stream load for struct type
3、m[key] function for map type
How to use:
Set the fe config to create table for struct and map type
1、admin set frontend config("enable_struct_type" = "true");
2、admin set frontend config("enable_map_type" = "true");
#16547
Co-authored-by: xy720 <xuyang25@baidu.com>
Co-authored-by: amory <wangqiannan@selectdb.com>
Co-authored-by: cambyzju <zhuxiaoli01@baidu.com>
Co-authored-by: hucheng01 <hucheng01@baidu.com>
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
Support iceberg schema evolution for parquet file format.
Iceberg use unique id for each column to support schema evolution.
To support this feature in Doris, FE side need to get the current column id for each column and send the ids to be side.
Be read column id from parquet key_value_metadata, set the changed column name in Block to match the name in parquet file before reading data. And set the name back after reading data.
Since Filesystem inherited std::enable_shared_from_this , it is dangerous to create native point of FileSystem.
To avoid this behavior, making the constructor of XxxFileSystem a private method and using the static method create(...) to get a new FileSystem object.
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>
This pr mainly to optimize the histogram(👉🏻https://github.com/apache/doris/pull/14910) aggregation function. Including the following:
1. Support input parameters `sample_rate` and `max_bucket_num`
2. Add UT and regression test
3. Add documentation
4. Optimize function implementation logic
Parameter description：
- `sample_rate`：Optional. The proportion of sample data used to generate the histogram. The default is 0.2.
- `max_bucket_num`：Optional. Limit the number of histogram buckets. The default value is 128.
---
Example：
```
MySQL [test]> SELECT histogram(c_float) FROM histogram_test;
+-------------------------------------------------------------------------------------------------------------------------------------+
| histogram(`c_float`) |
+-------------------------------------------------------------------------------------------------------------------------------------+
| {"sample_rate":0.2,"max_bucket_num":128,"bucket_num":3,"buckets":[{"lower":"0.1","upper":"0.1","count":1,"pre_sum":0,"ndv":1},...]} |
+-------------------------------------------------------------------------------------------------------------------------------------+
MySQL [test]> SELECT histogram(c_string, 0.5, 2) FROM histogram_test;
+-------------------------------------------------------------------------------------------------------------------------------------+
| histogram(`c_string`) |
+-------------------------------------------------------------------------------------------------------------------------------------+
| {"sample_rate":0.5,"max_bucket_num":2,"bucket_num":2,"buckets":[{"lower":"str1","upper":"str7","count":4,"pre_sum":0,"ndv":3},...]} |
+-------------------------------------------------------------------------------------------------------------------------------------+
```
Query result description：
```
{
"sample_rate": 0.2,
"max_bucket_num": 128,
"bucket_num": 3,
"buckets": [
{
"lower": "0.1",
"upper": "0.2",
"count": 2,
"pre_sum": 0,
"ndv": 2
},
{
"lower": "0.8",
"upper": "0.9",
"count": 2,
"pre_sum": 2,
"ndv": 2
},
{
"lower": "1.0",
"upper": "1.0",
"count": 2,
"pre_sum": 4,
"ndv": 1
}
]
}
```
Field description：
- sample_rate：Rate of sampling
- max_bucket_num：Limit the maximum number of buckets
- bucket_num：The actual number of buckets
- buckets：All buckets
- lower：Upper bound of the bucket
- upper：Lower bound of the bucket
- count：The number of elements contained in the bucket
- pre_sum：The total number of elements in the front bucket
- ndv：The number of different values in the bucket
> Total number of histogram elements = number of elements in the last bucket(count) + total number of elements in the previous bucket(pre_sum).
Fix three bugs when read iceberg v2 tables:
1. The `delete position` in `delete file` represents the position of delete row in the entire file, but the `read range` in
`RowGroupReader` represents the position in current row group. Therefore, we need to subtract the position of first
row of current row group from `delete position`.
2. When only reading the partition columns, `RowGroupReader` skips processing the `delete position`.
3. If the `delete position` has delete all rows in a row group, the `read range` is empty, but we read the whole row
group in such case.
Optimize four performance issues:
1. We change `delete position` to `delete range`, and then merge `delete range` and `read range` into the final read
ranges. This process is too tedious and time-consuming. . we can merge `delete position` and `read range` directly.
2. `delete position` is ordered in a `delete file`, so we can use merge-sort, instead of ordered-set.
3. Initialize `RowGroupReader` when reading, instead of initialize all row groups when opening a `ParquetReader`, to
save memory usage, and the same as `IcebergReader`.
4. Change the recursive call of `_do_lazy_read` to loop logic.
**Histogram statistics**
Currently doris collects statistics, but no histogram data, and by default the optimizer assumes that the different values of the columns are evenly distributed. This calculation can be problematic when the data distribution is skewed. So this pr implements the collection of histogram statistics.
For columns containing data skew columns (columns with unevenly distributed data in the column), histogram statistics enable the optimizer to generate more accurate estimates of cardinality for filtering or join predicates involving these columns, resulting in a more precise execution plan.
The optimization of the execution plan by histogram is mainly in two aspects: the selection of where condition and the selection of join order. The selection principle of the where condition is relatively simple: the histogram is used to calculate the selection rate of each predicate, and the filter with higher selection rate is preferred.
The selection of join order is based on the estimation of the number of rows in the join result. In the case of uneven data distribution in the join condition columns, histogram can greatly improve the accuracy of the prediction of the number of rows in the join result. At the same time, if the number of rows of a bucket in one of the columns is 0, you can mark it and directly skip the bucket in the subsequent join process to improve efficiency.
---
Histogram statistics are mainly collected by the histogram aggregation function, which is used as follows:
**Syntax**
```SQL
histogram(expr)
```
> The histogram function is used to describe the distribution of the data. It uses an "equal height" bucking strategy, and divides the data into buckets according to the value of the data. It describes each bucket with some simple data, such as the number of values that fall in the bucket. It is mainly used by the optimizer to estimate the range query.
**example**
```
MySQL [test]> select histogram(login_time) from dev_table;
+------------------------------------------------------------------------------------------------------------------------------+
| histogram(`login_time`) |
+------------------------------------------------------------------------------------------------------------------------------+
| {"bucket_size":5,"buckets":[{"lower":"2022-09-21 17:30:29","upper":"2022-09-21 22:30:29","count":9,"pre_sum":0,"ndv":1},...]}|
+------------------------------------------------------------------------------------------------------------------------------+
```
**description**
```JSON
{
"bucket_size": 5,
"buckets": [
{
"lower": "2022-09-21 17:30:29",
"upper": "2022-09-21 22:30:29",
"count": 9,
"pre_sum": 0,
"ndv": 1
},
{
"lower": "2022-09-22 17:30:29",
"upper": "2022-09-22 22:30:29",
"count": 10,
"pre_sum": 9,
"ndv": 1
},
{
"lower": "2022-09-23 17:30:29",
"upper": "2022-09-23 22:30:29",
"count": 9,
"pre_sum": 19,
"ndv": 1
},
{
"lower": "2022-09-24 17:30:29",
"upper": "2022-09-24 22:30:29",
"count": 9,
"pre_sum": 28,
"ndv": 1
},
{
"lower": "2022-09-25 17:30:29",
"upper": "2022-09-25 22:30:29",
"count": 9,
"pre_sum": 37,
"ndv": 1
}
]
}
```
TODO:
- histogram func supports parameter and sample statistics (It's got another pr)
- use histogram statistics
- add p0 regression
The segment group is useless in current codebase, remove all the related code inside Doris. As for the related protobuf code, use reserved flag to prevent any future user from using that field.
