Add a new field `Lag` in result of `show routine load` stmt.
`Lag: {"0":10, "1":0}` means kafka partition 0 has 10 msg behind and partition 1 is update-to-date.
Users can directly query the data in the hive table in Doris, and can use join to perform complex queries without laboriously importing data from hive.
Main changes list below:
FE:
Extend HiveScanNode from BrokerScanNode
HiveMetaStoreClientHelper communicate with HIVE and HDFS.
BE:
Treate HiveScanNode as BrokerScanNode, treate HiveTable as BrokerTable.
broker_scanner.cpp: suppot read column from HDFS path.
orc_scanner.cpp: support read hdfs file.
POM:
Add hive.version=2.3.7, hive-metastore and hive-exec
Add hadoop.version=2.8.0, hadoop-hdfs
Upgrade commons-lang to fix incompatiblity of Java 9 and later.
Thrift:
Add THiveTable
Add read_by_column_def in TBrokerRangeDesc
The new session variable 'close_join_reorder' is used to turn off all automatic join reorder algorithms.
If close_join_reorder is true, the Doris will execute query by the order in the original query.
1. Migrate some of the best practice articles to the Blog
2. Changed the names of performance tests and best practices to performance tests and examples
Mainly changes:
1. Fix [Bug] Colocate group can not redistributed after dropping a backend #7019
2. Add detail msg about why a colocate group is unstable.
3. Add more suggestion when upgrading Doris cluster.
Add the sharing blog function to the document site, including the blog list and detail page. At the same time, a guide on how to share blogs has been added to the developer guide.
Added bprc stub cache check and reset api, used to test whether the bprc stub cache is available, and reset the bprc stub cache
add a config used for auto check and reset bprc stub
Doris should provide a http api to return backends list for connectors to submit stream load,
and without privilege checking, which can let common user to use it
1. By default , Spark connector must write all fields value to `Doris` table .
In this feature , user can specify part of fields to write , even specify the order of the fields to write.
eg:
I have a table named `student` which has three columns (name,gender,age) ,
creating table sql as following:
```sql
create table student (name varchar(255), gender varchar(10), age int) duplicate key (name) distributed by hash(name) buckets 2;
```
Now , I just want to write values to two columns : name , gender.
The code as following:
```scala
val df = spark.createDataFrame(Seq(
("m", "zhangsan"),
("f", "lisi"),
("m", "wangwu")
))
df.write
.format("doris")
.option("doris.fenodes", dorisFeNodes)
.option("doris.table.identifier", dorisTable)
.option("user", dorisUser)
.option("password", dorisPwd)
//specify your fields or the order
.option("doris.write.field", "gender,name")
.save()
```
## Case
In the load process, each tablet will have a memtable to save the incoming data,
and if the data in a memtable is larger than 100MB, it will be flushed to disk as a `segment` file. And then
a new memtable will be created to save the following data/
Assume that this is a table with N buckets(tablets). So the max size of all memtables will be `N * 100MB`.
If N is large, it will cost too much memory.
So for memory limit purpose, when the size of all memtables reach a threshold(2GB as default), Doris will
try to flush all current memtables to disk(even if their size are not reach 100MB).
So you will see that the memtable will be flushed when it's size reach `2GB/N`, which maybe much smaller
than 100MB, resulting in too many small segment files.
## Solution
When decide to flush memtable to reduce memory consumption, NOT to flush all memtable, but to flush part
of them.
For example, there are 50 tablets(with 50 memtables). The memory limit is 1GB, so when each memtable reach
20MB, the total size reach 1GB, and flush will occur.
If I only flush 25 of 50 memtables, then next time when the total size reach 1GB, there will be 25 memtables with
size 10MB, and other 25 memtables with size 30MB. So I can flush those memtables with size 30MB, which is larger
than 20MB.
The main idea is to introduce some jitter during flush to ensure the small unevenness of each memtable, so as to ensure that flush will only be triggered when the memtable is large enough.
In my test, loading a table with 48 buckets, mem limit 2G, in previous version, the average memtable size is 44MB,
after modification, the average size is 82MB
Add a use_path_style property for S3
Upgrade hadoop-common and hadoop-aws to 2.8.0 to support path style property
Fix some S3 URI bugs
Add some logs for tracing load process.
1. optimize error message when using batch delete
2. rename session variable is_report_success to enable_profile
3. add table name to OlapScanner profile
This CL mainly changes:
1. Add star schema benchmark tools in `tools/ssb-tools`, for user to easy load and test with SSB data set.
2. Disable the segment cache for some read scenario such as compaction and alter operation.(Fix#6924 )
3. Fix a bug that `max_segment_num_per_rowset` won't work(Fix#6926)
4. Enable `enable_batch_delete_by_default` by default.
1. Simplify the use of flink connector like other stream sink by GenericDorisSinkFunction.
2. Add the use cases of flink connector.
## Use case
```
env.fromElements("{\"longitude\": \"116.405419\", \"city\": \"北京\", \"latitude\": \"39.916927\"}")
.addSink(
DorisSink.sink(
DorisOptions.builder()
.setFenodes("FE_IP:8030")
.setTableIdentifier("db.table")
.setUsername("root")
.setPassword("").build()
));
```
With thirdparties 1.4.0 to 1.4.1
1. Add patch for aws-c-cal-0.4.5
2. Add some solutions for `undefined reference libpsl`
3. Move libgsasl to fix link problme of libcurl.
4. Downgrade openssl to 1.0.2k to fix problem of low version glibc
