The jar file compiled by Flink and Spark Connector, with the corresponding Flink, Spark version
and Scala version at compile time, so that users can know whether the version number matches when using it.
Example of output file name：doris-spark-1.0.0-spark-3.2.0_2.12.jar
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.
Checking _encoding_map in the original code to return in advance will cause some encoding methods cannot be pushed to default_encoding_type_map_ or value_seek_encoding_map_ in EncodingInfoResolver constructor.
E.g:
EncodingInfoResolver::EncodingInfoResolver() {
....
_add_map<OLAP_FIELD_TYPE_BOOL, PLAIN_ENCODING>();
_add_map<OLAP_FIELD_TYPE_BOOL, PLAIN_ENCODING, true>();
...
}
The second line code is invilid.
The union(set operation) stmt also need to analyze 'OutFileClause'.
Whether the fragment is colocate only needs to check the plan node belonging to this fragment.
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
schema change fail as memory allocation fail on row block sorting, however, it should do internal sorting first before schema change fail as memory allocation fail on row block sorting in case there are enough memory after internal sorting.
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()
```
schema change fail as memory allocation fail on row block sorting.
however, it should do internal sorting first before schema change fail
as memory allocation fail on row block sorting in case there are enough
memory after internal sorting.
## 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.
