[
https://issues.apache.org/jira/browse/SPARK-8406?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cheng Lian updated SPARK-8406:
------------------------------
Description:
To support appending, the Parquet data source tries to find out the max part
number of part-files in the destination directory (the <id> in output file name
"part-r-<id>.gz.parquet") at the beginning of the write job. In 1.3.0, this
step happens on driver side before any files are written. However, in 1.4.0,
this is moved to task side. Thus, for tasks scheduled later, they may see wrong
max part number generated by newly written files by other finished tasks within
the same job. This actually causes a race condition. In most cases, this only
causes nonconsecutive IDs in output file names. But when the DataFrame contains
thousands of RDD partitions, it's likely that two tasks may choose the same
part number, thus one of them gets overwritten by the other.
The data loss situation is not quite easy to reproduce. But the following Spark
shell snippet can reproduce nonconsecutive output file IDs:
{code}
sqlContext.range(0, 128).repartition(16).write.mode("overwrite").parquet("foo")
{code}
"16" can be replaced with any integer that is greater than the default
parallelism on your machine (usually it means core number, on my machine it's
8).
{noformat}
-rw-r--r-- 3 lian supergroup 0 2015-06-17 00:06
/user/lian/foo/_SUCCESS
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00001.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00002.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00003.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00004.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00005.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00006.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00007.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00008.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00017.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00018.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00019.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00020.gz.parquet
-rw-r--r-- 3 lian supergroup 352 2015-06-17 00:06
/user/lian/foo/part-r-00021.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00022.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00023.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00024.gz.parquet
{noformat}
Notice that the newly added ORC data source doesn't suffer this issue because
it uses both part number and {{System.currentTimeMills()}} to generate the
output file name.
was:
To support appending, the Parquet data source tries to find out the max ID of
part-files in the destination directory (the <id> in output file name
"part-r-<id>.gz.parquet") at the beginning of the write job. In 1.3.0, this
step happens on driver side before any files are written. However, in 1.4.0,
this is moved to task side. Thus, for tasks scheduled later, they may see wrong
max ID generated by newly written files by other finished tasks within the same
job. This actually causes a race condition. In most cases, this only causes
nonconsecutive IDs in output file names. But when the DataFrame contains
thousands of RDD partitions, it's likely that two tasks may choose the same ID,
thus one of them gets overwritten by the other.
The data loss situation is not quite easy to reproduce. But the following Spark
shell snippet can reproduce nonconsecutive output file IDs:
{code}
sqlContext.range(0, 128).repartition(16).write.mode("overwrite").parquet("foo")
{code}
"16" can be replaced with any integer that is greater than the default
parallelism on your machine (usually it means core number, on my machine it's
8).
{noformat}
-rw-r--r-- 3 lian supergroup 0 2015-06-17 00:06
/user/lian/foo/_SUCCESS
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00001.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00002.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00003.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00004.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00005.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00006.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00007.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00008.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00017.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00018.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00019.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00020.gz.parquet
-rw-r--r-- 3 lian supergroup 352 2015-06-17 00:06
/user/lian/foo/part-r-00021.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00022.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00023.gz.parquet
-rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
/user/lian/foo/part-r-00024.gz.parquet
{noformat}
Notice that the newly added ORC data source doesn't suffer this issue because
it uses both task ID and {{System.currentTimeMills()}} to generate the output
file name.
> Race condition when writing Parquet files
> -----------------------------------------
>
> Key: SPARK-8406
> URL: https://issues.apache.org/jira/browse/SPARK-8406
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.0
> Reporter: Cheng Lian
> Assignee: Cheng Lian
> Priority: Blocker
>
> To support appending, the Parquet data source tries to find out the max part
> number of part-files in the destination directory (the <id> in output file
> name "part-r-<id>.gz.parquet") at the beginning of the write job. In 1.3.0,
> this step happens on driver side before any files are written. However, in
> 1.4.0, this is moved to task side. Thus, for tasks scheduled later, they may
> see wrong max part number generated by newly written files by other finished
> tasks within the same job. This actually causes a race condition. In most
> cases, this only causes nonconsecutive IDs in output file names. But when the
> DataFrame contains thousands of RDD partitions, it's likely that two tasks
> may choose the same part number, thus one of them gets overwritten by the
> other.
> The data loss situation is not quite easy to reproduce. But the following
> Spark shell snippet can reproduce nonconsecutive output file IDs:
> {code}
> sqlContext.range(0,
> 128).repartition(16).write.mode("overwrite").parquet("foo")
> {code}
> "16" can be replaced with any integer that is greater than the default
> parallelism on your machine (usually it means core number, on my machine it's
> 8).
> {noformat}
> -rw-r--r-- 3 lian supergroup 0 2015-06-17 00:06
> /user/lian/foo/_SUCCESS
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00001.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00002.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00003.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00004.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00005.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00006.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00007.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00008.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00017.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00018.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00019.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00020.gz.parquet
> -rw-r--r-- 3 lian supergroup 352 2015-06-17 00:06
> /user/lian/foo/part-r-00021.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00022.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00023.gz.parquet
> -rw-r--r-- 3 lian supergroup 353 2015-06-17 00:06
> /user/lian/foo/part-r-00024.gz.parquet
> {noformat}
> Notice that the newly added ORC data source doesn't suffer this issue because
> it uses both part number and {{System.currentTimeMills()}} to generate the
> output file name.
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