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## What changes were proposed in this pull request? Before 2.2, MLlib keep to remove APIs deprecated in last feature/minor release. But from Spark 2.2, we decide to remove deprecated APIs in a major release, so we need to change corresponding annotations to tell users those will be removed in 3.0. Meanwhile, this fixed bugs in ML documents. The original ML docs can't show deprecated annotations in ```MLWriter``` and ```MLReader``` related class, we correct it in this PR. Before:  After:  ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17946 from yanboliang/spark-20707.
## What changes were proposed in this pull request? Review new Scala APIs introduced in 2.2. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17934 from yanboliang/spark-20501.
…hods Update ALS examples illustrating use of "recommendForAllX" methods. ## How was this patch tested? Built and ran examples locally Author: Nick Pentreath <nickp@za.ibm.com> Closes #17950 from MLnick/SPARK-20553-update-als-examples.
Small clean ups from #17742 and #17845. ## How was this patch tested? Existing unit tests. Author: Nick Pentreath <nickp@za.ibm.com> Closes #17919 from MLnick/SPARK-20677-als-perf-followup.
## What changes were proposed in this pull request? In the current codes, when worker connects to master, master will send its address to the worker. Then worker will save this address and use it to reconnect in case of failure. However, sometimes, this address is not correct. If there is a proxy between master and worker, the address master sent is not the address of proxy. In this PR, the master address used by the worker will be sent to the master, then master just replies this address back, worker will use this address to reconnect in case of failure. In other words, the worker will use the config master address set in the worker side if possible rather than the master address set in the master side. There is still one potential issue though. When a master is restarted or takes over leadership, the work will use the address sent from the master to connect. If there is still a proxy between master and worker, the address may be wrong. However, there is no way to figure it out just in the worker. ## How was this patch tested? The new added unit test. Author: Shixiong Zhu <shixiong@databricks.com> Closes #17821 from zsxwing/SPARK-20529.
…o 64KB bytecode size limit ## What changes were proposed in this pull request? When an expression for `df.filter()` has many nodes (e.g. 400), the size of Java bytecode for the generated Java code is more than 64KB. It produces an Java exception. As a result, the execution fails. This PR continues to execute by calling `Expression.eval()` disabling code generation if an exception has been caught. ## How was this patch tested? Add a test suite into `DataFrameSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #17087 from kiszk/SPARK-19372.
…tries ## What changes were proposed in this pull request? The pull requests proposes to remove the hardcoded values for Amazon Kinesis - MIN_RETRY_WAIT_TIME_MS, MAX_RETRIES. This change is critical for kinesis checkpoint recovery when the kinesis backed rdd is huge. Following happens in a typical kinesis recovery : - kinesis throttles large number of requests while recovering - retries in case of throttling are not able to recover due to the small wait period - kinesis throttles per second, the wait period should be configurable for recovery The patch picks the spark kinesis configs from: - spark.streaming.kinesis.retry.wait.time - spark.streaming.kinesis.retry.max.attempts Jira : https://issues.apache.org/jira/browse/SPARK-20140 ## How was this patch tested? Modified the KinesisBackedBlockRDDSuite.scala to run kinesis tests with the modified configurations. Wasn't able to test the patch with actual throttling. Author: Yash Sharma <ysharma@atlassian.com> Closes #17467 from yssharma/ysharma/spark-kinesis-retries.
## What changes were proposed in this pull request? Currently the parser logs the query it is parsing at `info` level. This is too high, this PR lowers the log level to `debug`. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #18006 from hvanhovell/lower_parser_log_level.
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### What changes were proposed in this pull request?
Push down filter through expand. For case below:
```
create table t1(pid int, uid int, sid int, dt date, suid int) using parquet;
create table t2(pid int, vs int, uid int, csid int) using parquet;
SELECT
years,
appversion,
SUM(uusers) AS users
FROM (SELECT
Date_trunc('year', dt) AS years,
CASE
WHEN h.pid = 3 THEN 'iOS'
WHEN h.pid = 4 THEN 'Android'
ELSE 'Other'
END AS viewport,
h.vs AS appversion,
Count(DISTINCT u.uid) AS uusers
,Count(DISTINCT u.suid) AS srcusers
FROM t1 u
join t2 h
ON h.uid = u.uid
GROUP BY 1,
2,
3) AS a
WHERE viewport = 'iOS'
GROUP BY 1,
2
```
Plan. before this pr:
```
== Physical Plan ==
*(5) HashAggregate(keys=[years#30, appversion#32], functions=[sum(uusers#33L)])
+- Exchange hashpartitioning(years#30, appversion#32, 200), true, [id=#251]
+- *(4) HashAggregate(keys=[years#30, appversion#32], functions=[partial_sum(uusers#33L)])
+- *(4) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[count(if ((gid#44 = 1)) u.`uid`#47 else null)])
+- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, 200), true, [id=#246]
+- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[partial_count(if ((gid#44 = 1)) u.`uid`#47 else null)])
+- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
+- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44, 200), true, [id=#241]
+- *(2) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
+- *(2) Filter (CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46 = iOS)
+- *(2) Expand [ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, uid#7, null, 1), ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, null, suid#10, 2)], [date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44]
+- *(2) Project [uid#7, dt#9, suid#10, pid#11, vs#12]
+- *(2) BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight
:- *(2) Project [uid#7, dt#9, suid#10]
: +- *(2) Filter isnotnull(uid#7)
: +- *(2) ColumnarToRow
: +- FileScan parquet default.t1[uid#7,dt#9,suid#10] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t1], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date,suid:int>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, true] as bigint))), [id=#233]
+- *(1) Project [pid#11, vs#12, uid#13]
+- *(1) Filter isnotnull(uid#13)
+- *(1) ColumnarToRow
+- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [isnotnull(uid#13)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t2], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```
Plan. after. this pr. :
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[years#0, appversion#2], functions=[sum(uusers#3L)], output=[years#0, appversion#2, users#5L])
+- Exchange hashpartitioning(years#0, appversion#2, 5), true, [id=#71]
+- HashAggregate(keys=[years#0, appversion#2], functions=[partial_sum(uusers#3L)], output=[years#0, appversion#2, sum#22L])
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[count(distinct uid#7)], output=[years#0, appversion#2, uusers#3L])
+- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, 5), true, [id=#67]
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[partial_count(distinct uid#7)], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, count#27L])
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
+- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7, 5), true, [id=#63]
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles)) AS date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END AS CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
+- Project [uid#7, dt#9, pid#11, vs#12]
+- BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight, false
:- Filter isnotnull(uid#7)
: +- FileScan parquet default.t1[uid#7,dt#9] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, false] as bigint)),false), [id=#58]
+- Filter ((CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS) AND isnotnull(uid#13))
+- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [(CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS), isnotnull..., Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```
### Why are the changes needed?
Improve performance, filter more data.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes apache#30278 from AngersZhuuuu/SPARK-33302.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
GulajavaMinistudio
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Jan 18, 2024
### What changes were proposed in this pull request? Purge pip cache in dockerfile ### Why are the changes needed? to save 4~5G disk space: before https://github.com/zhengruifeng/spark/actions/runs/7541725028/job/20530432798 ``` #45 [39/39] RUN df -h #45 0.090 Filesystem Size Used Avail Use% Mounted on #45 0.090 overlay 84G 70G 15G 83% / #45 0.090 tmpfs 64M 0 64M 0% /dev #45 0.090 shm 64M 0 64M 0% /dev/shm #45 0.090 /dev/root 84G 70G 15G 83% /etc/resolv.conf #45 0.090 tmpfs 7.9G 0 7.9G 0% /proc/acpi #45 0.090 tmpfs 7.9G 0 7.9G 0% /sys/firmware #45 0.090 tmpfs 7.9G 0 7.9G 0% /proc/scsi #45 DONE 2.0s ``` after https://github.com/zhengruifeng/spark/actions/runs/7549204209/job/20552796796 ``` #48 [42/43] RUN python3.12 -m pip cache purge #48 0.670 Files removed: 392 #48 DONE 0.7s #49 [43/43] RUN df -h #49 0.075 Filesystem Size Used Avail Use% Mounted on #49 0.075 overlay 84G 65G 19G 79% / #49 0.075 tmpfs 64M 0 64M 0% /dev #49 0.075 shm 64M 0 64M 0% /dev/shm #49 0.075 /dev/root 84G 65G 19G 79% /etc/resolv.conf #49 0.075 tmpfs 7.9G 0 7.9G 0% /proc/acpi #49 0.075 tmpfs 7.9G 0 7.9G 0% /sys/firmware #49 0.075 tmpfs 7.9G 0 7.9G 0% /proc/scsi ``` ### Does this PR introduce _any_ user-facing change? no, infra-only ### How was this patch tested? ci ### Was this patch authored or co-authored using generative AI tooling? no Closes apache#44768 from zhengruifeng/infra_docker_cleanup. Authored-by: Ruifeng Zheng <ruifengz@apache.org> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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