Re: Parquet Hive table become very slow on 1.3?
Xudong and Rex, Can you try 1.3.1? With PR 5339 http://github.com/apache/spark/pull/5339 , after we get a hive parquet from metastore and convert it to our native parquet code path, we will cache the converted relation. For now, the first access to that hive parquet table reads all of the footers (when you first refer to that table in a query or call sqlContext.table(hiveParquetTable)). All of your later accesses will hit the metadata cache. Thanks, Yin On Tue, Apr 21, 2015 at 1:13 AM, Rex Xiong bycha...@gmail.com wrote: We have the similar issue with massive parquet files, Cheng Lian, could you have a look? 2015-04-08 15:47 GMT+08:00 Zheng, Xudong dong...@gmail.com: Hi Cheng, I tried both these patches, and seems still not resolve my issue. And I found the most time is spend on this line in newParquet.scala: ParquetFileReader.readAllFootersInParallel( sparkContext.hadoopConfiguration, seqAsJavaList(leaves), taskSideMetaData) Which need read all the files under the Parquet folder, while our Parquet folder has a lot of Parquet files (near 2000), read one file need about 2 seconds, so it become very slow ... And the PR 5231 did not skip this steps so it not resolve my issue. As our Parquet files are generated by a Spark job, so the number of .parquet files is same with the number of tasks, that is why we have so many files. But these files actually have the same schema. Is there any way to merge these files into one, or avoid scan each of them? On Sat, Apr 4, 2015 at 9:47 PM, Cheng Lian lian.cs@gmail.com wrote: Hey Xudong, We had been digging this issue for a while, and believe PR 5339 http://github.com/apache/spark/pull/5339 and PR 5334 http://github.com/apache/spark/pull/5339 should fix this issue. There two problems: 1. Normally we cache Parquet table metadata for better performance, but when converting Hive metastore Hive tables, the cache is not used. Thus heavy operations like schema discovery is done every time a metastore Parquet table is converted. 2. With Parquet task side metadata reading (which is turned on by default), we can actually skip the row group information in the footer. However, we accidentally called a Parquet function which doesn't skip row group information. For your question about schema merging, Parquet allows different part-files have different but compatible schemas. For example, part-1.parquet has columns a and b, while part-2.parquet may has columns a and c. In some cases, the summary files (_metadata and _common_metadata) contains the merged schema (a, b, and c), but it's not guaranteed. For example, when the user defined metadata stored different part-files contain different values for the same key, Parquet simply gives up writing summary files. That's why all part-files must be touched to get a precise merged schema. However, in scenarios where a centralized arbitrative schema is available (e.g. Hive metastore schema, or the schema provided by user via data source DDL), we don't need to do schema merging on driver side, but defer it to executor side and each task only needs to reconcile those part-files it needs to touch. This is also what the Parquet developers did recently for parquet-hadoop https://github.com/apache/incubator-parquet-mr/pull/45. Cheng On 3/31/15 11:49 PM, Zheng, Xudong wrote: Thanks Cheng! Set 'spark.sql.parquet.useDataSourceApi' to false resolves my issues, but the PR 5231 seems not. Not sure any other things I did wrong ... BTW, actually, we are very interested in the schema merging feature in Spark 1.3, so both these two solution will disable this feature, right? It seems that Parquet metadata is store in a file named _metadata in the Parquet file folder (each folder is a partition as we use partition table), why we need scan all Parquet part files? Is there any other solutions could keep schema merging feature at the same time? We are really like this feature :) On Tue, Mar 31, 2015 at 3:19 PM, Cheng Lian lian.cs@gmail.com wrote: Hi Xudong, This is probably because of Parquet schema merging is turned on by default. This is generally useful for Parquet files with different but compatible schemas. But it needs to read metadata from all Parquet part-files. This can be problematic when reading Parquet files with lots of part-files, especially when the user doesn't need schema merging. This issue is tracked by SPARK-6575, and here is a PR for it: https://github.com/apache/spark/pull/5231. This PR adds a configuration to disable schema merging by default when doing Hive metastore Parquet table conversion. Another workaround is to fallback to the old Parquet code by setting spark.sql.parquet.useDataSourceApi to false. Cheng On 3/31/15 2:47 PM, Zheng, Xudong wrote: Hi all, We are using Parquet Hive table, and we are upgrading to Spark 1.3. But we find that, just a simple COUNT(*) query will much slower (100x) than Spark
Re: Parquet Hive table become very slow on 1.3?
Yin, Thanks for your reply. We already patched this PR to our 1.3.0 As Xudong mentioned, we have thousand of parquet files, it's very very slow in first read, and another app will add more files and refresh table regularly. Cheng Lian's PR-5334 seems can resolve this issue, it will skip read all footer if we set auto merge to false. But it's not done yet. Thanks 2015-04-22 23:10 GMT+08:00 Yin Huai yh...@databricks.com: Xudong and Rex, Can you try 1.3.1? With PR 5339 http://github.com/apache/spark/pull/5339 , after we get a hive parquet from metastore and convert it to our native parquet code path, we will cache the converted relation. For now, the first access to that hive parquet table reads all of the footers (when you first refer to that table in a query or call sqlContext.table(hiveParquetTable)). All of your later accesses will hit the metadata cache. Thanks, Yin On Tue, Apr 21, 2015 at 1:13 AM, Rex Xiong bycha...@gmail.com wrote: We have the similar issue with massive parquet files, Cheng Lian, could you have a look? 2015-04-08 15:47 GMT+08:00 Zheng, Xudong dong...@gmail.com: Hi Cheng, I tried both these patches, and seems still not resolve my issue. And I found the most time is spend on this line in newParquet.scala: ParquetFileReader.readAllFootersInParallel( sparkContext.hadoopConfiguration, seqAsJavaList(leaves), taskSideMetaData) Which need read all the files under the Parquet folder, while our Parquet folder has a lot of Parquet files (near 2000), read one file need about 2 seconds, so it become very slow ... And the PR 5231 did not skip this steps so it not resolve my issue. As our Parquet files are generated by a Spark job, so the number of .parquet files is same with the number of tasks, that is why we have so many files. But these files actually have the same schema. Is there any way to merge these files into one, or avoid scan each of them? On Sat, Apr 4, 2015 at 9:47 PM, Cheng Lian lian.cs@gmail.com wrote: Hey Xudong, We had been digging this issue for a while, and believe PR 5339 http://github.com/apache/spark/pull/5339 and PR 5334 http://github.com/apache/spark/pull/5339 should fix this issue. There two problems: 1. Normally we cache Parquet table metadata for better performance, but when converting Hive metastore Hive tables, the cache is not used. Thus heavy operations like schema discovery is done every time a metastore Parquet table is converted. 2. With Parquet task side metadata reading (which is turned on by default), we can actually skip the row group information in the footer. However, we accidentally called a Parquet function which doesn't skip row group information. For your question about schema merging, Parquet allows different part-files have different but compatible schemas. For example, part-1.parquet has columns a and b, while part-2.parquet may has columns a and c. In some cases, the summary files (_metadata and _common_metadata) contains the merged schema (a, b, and c), but it's not guaranteed. For example, when the user defined metadata stored different part-files contain different values for the same key, Parquet simply gives up writing summary files. That's why all part-files must be touched to get a precise merged schema. However, in scenarios where a centralized arbitrative schema is available (e.g. Hive metastore schema, or the schema provided by user via data source DDL), we don't need to do schema merging on driver side, but defer it to executor side and each task only needs to reconcile those part-files it needs to touch. This is also what the Parquet developers did recently for parquet-hadoop https://github.com/apache/incubator-parquet-mr/pull/45. Cheng On 3/31/15 11:49 PM, Zheng, Xudong wrote: Thanks Cheng! Set 'spark.sql.parquet.useDataSourceApi' to false resolves my issues, but the PR 5231 seems not. Not sure any other things I did wrong ... BTW, actually, we are very interested in the schema merging feature in Spark 1.3, so both these two solution will disable this feature, right? It seems that Parquet metadata is store in a file named _metadata in the Parquet file folder (each folder is a partition as we use partition table), why we need scan all Parquet part files? Is there any other solutions could keep schema merging feature at the same time? We are really like this feature :) On Tue, Mar 31, 2015 at 3:19 PM, Cheng Lian lian.cs@gmail.com wrote: Hi Xudong, This is probably because of Parquet schema merging is turned on by default. This is generally useful for Parquet files with different but compatible schemas. But it needs to read metadata from all Parquet part-files. This can be problematic when reading Parquet files with lots of part-files, especially when the user doesn't need schema merging. This issue is tracked by SPARK-6575, and here is a PR for it: https://github.com/apache/spark/pull/5231. This PR adds a
Re: Parquet Hive table become very slow on 1.3?
We have the similar issue with massive parquet files, Cheng Lian, could you
have a look?
2015-04-08 15:47 GMT+08:00 Zheng, Xudong dong...@gmail.com:
Hi Cheng,
I tried both these patches, and seems still not resolve my issue. And I
found the most time is spend on this line in newParquet.scala:
ParquetFileReader.readAllFootersInParallel(
sparkContext.hadoopConfiguration, seqAsJavaList(leaves),
taskSideMetaData)
Which need read all the files under the Parquet folder, while our Parquet
folder has a lot of Parquet files (near 2000), read one file need about 2
seconds, so it become very slow ... And the PR 5231 did not skip this steps
so it not resolve my issue.
As our Parquet files are generated by a Spark job, so the number of
.parquet files is same with the number of tasks, that is why we have so
many files. But these files actually have the same schema. Is there any way
to merge these files into one, or avoid scan each of them?
On Sat, Apr 4, 2015 at 9:47 PM, Cheng Lian lian.cs@gmail.com wrote:
Hey Xudong,
We had been digging this issue for a while, and believe PR 5339
http://github.com/apache/spark/pull/5339 and PR 5334
http://github.com/apache/spark/pull/5339 should fix this issue.
There two problems:
1. Normally we cache Parquet table metadata for better performance, but
when converting Hive metastore Hive tables, the cache is not used. Thus
heavy operations like schema discovery is done every time a metastore
Parquet table is converted.
2. With Parquet task side metadata reading (which is turned on by
default), we can actually skip the row group information in the footer.
However, we accidentally called a Parquet function which doesn't skip row
group information.
For your question about schema merging, Parquet allows different
part-files have different but compatible schemas. For example,
part-1.parquet has columns a and b, while part-2.parquet may has
columns a and c. In some cases, the summary files (_metadata and
_common_metadata) contains the merged schema (a, b, and c), but it's not
guaranteed. For example, when the user defined metadata stored different
part-files contain different values for the same key, Parquet simply gives
up writing summary files. That's why all part-files must be touched to get
a precise merged schema.
However, in scenarios where a centralized arbitrative schema is available
(e.g. Hive metastore schema, or the schema provided by user via data source
DDL), we don't need to do schema merging on driver side, but defer it to
executor side and each task only needs to reconcile those part-files it
needs to touch. This is also what the Parquet developers did recently for
parquet-hadoop https://github.com/apache/incubator-parquet-mr/pull/45.
Cheng
On 3/31/15 11:49 PM, Zheng, Xudong wrote:
Thanks Cheng!
Set 'spark.sql.parquet.useDataSourceApi' to false resolves my issues,
but the PR 5231 seems not. Not sure any other things I did wrong ...
BTW, actually, we are very interested in the schema merging feature in
Spark 1.3, so both these two solution will disable this feature, right? It
seems that Parquet metadata is store in a file named _metadata in the
Parquet file folder (each folder is a partition as we use partition table),
why we need scan all Parquet part files? Is there any other solutions could
keep schema merging feature at the same time? We are really like this
feature :)
On Tue, Mar 31, 2015 at 3:19 PM, Cheng Lian lian.cs@gmail.com
wrote:
Hi Xudong,
This is probably because of Parquet schema merging is turned on by
default. This is generally useful for Parquet files with different but
compatible schemas. But it needs to read metadata from all Parquet
part-files. This can be problematic when reading Parquet files with lots of
part-files, especially when the user doesn't need schema merging.
This issue is tracked by SPARK-6575, and here is a PR for it:
https://github.com/apache/spark/pull/5231. This PR adds a configuration
to disable schema merging by default when doing Hive metastore Parquet
table conversion.
Another workaround is to fallback to the old Parquet code by setting
spark.sql.parquet.useDataSourceApi to false.
Cheng
On 3/31/15 2:47 PM, Zheng, Xudong wrote:
Hi all,
We are using Parquet Hive table, and we are upgrading to Spark 1.3.
But we find that, just a simple COUNT(*) query will much slower (100x) than
Spark 1.2.
I find the most time spent on driver to get HDFS blocks. I find large
amount of get below logs printed:
15/03/30 23:03:43 DEBUG ProtobufRpcEngine: Call: getBlockLocations took
2097ms
15/03/30 23:03:43 DEBUG DFSClient: newInfo = LocatedBlocks{
fileLength=77153436
underConstruction=false
blocks=[LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948_1448275;
getBlockSize()=77153436; corrupt=false; offset=0;
locs=[10.152.116.172:50010, 10.152.116.169:50010, 10.153.125.184:50010]}]
Re: Parquet Hive table become very slow on 1.3?
Hi Cheng,
I tried both these patches, and seems still not resolve my issue. And I
found the most time is spend on this line in newParquet.scala:
ParquetFileReader.readAllFootersInParallel(
sparkContext.hadoopConfiguration, seqAsJavaList(leaves), taskSideMetaData)
Which need read all the files under the Parquet folder, while our Parquet
folder has a lot of Parquet files (near 2000), read one file need about 2
seconds, so it become very slow ... And the PR 5231 did not skip this steps
so it not resolve my issue.
As our Parquet files are generated by a Spark job, so the number of
.parquet files is same with the number of tasks, that is why we have so
many files. But these files actually have the same schema. Is there any way
to merge these files into one, or avoid scan each of them?
On Sat, Apr 4, 2015 at 9:47 PM, Cheng Lian lian.cs@gmail.com wrote:
Hey Xudong,
We had been digging this issue for a while, and believe PR 5339
http://github.com/apache/spark/pull/5339 and PR 5334
http://github.com/apache/spark/pull/5339 should fix this issue.
There two problems:
1. Normally we cache Parquet table metadata for better performance, but
when converting Hive metastore Hive tables, the cache is not used. Thus
heavy operations like schema discovery is done every time a metastore
Parquet table is converted.
2. With Parquet task side metadata reading (which is turned on by
default), we can actually skip the row group information in the footer.
However, we accidentally called a Parquet function which doesn't skip row
group information.
For your question about schema merging, Parquet allows different
part-files have different but compatible schemas. For example,
part-1.parquet has columns a and b, while part-2.parquet may has
columns a and c. In some cases, the summary files (_metadata and
_common_metadata) contains the merged schema (a, b, and c), but it's not
guaranteed. For example, when the user defined metadata stored different
part-files contain different values for the same key, Parquet simply gives
up writing summary files. That's why all part-files must be touched to get
a precise merged schema.
However, in scenarios where a centralized arbitrative schema is available
(e.g. Hive metastore schema, or the schema provided by user via data source
DDL), we don't need to do schema merging on driver side, but defer it to
executor side and each task only needs to reconcile those part-files it
needs to touch. This is also what the Parquet developers did recently for
parquet-hadoop https://github.com/apache/incubator-parquet-mr/pull/45.
Cheng
On 3/31/15 11:49 PM, Zheng, Xudong wrote:
Thanks Cheng!
Set 'spark.sql.parquet.useDataSourceApi' to false resolves my issues,
but the PR 5231 seems not. Not sure any other things I did wrong ...
BTW, actually, we are very interested in the schema merging feature in
Spark 1.3, so both these two solution will disable this feature, right? It
seems that Parquet metadata is store in a file named _metadata in the
Parquet file folder (each folder is a partition as we use partition table),
why we need scan all Parquet part files? Is there any other solutions could
keep schema merging feature at the same time? We are really like this
feature :)
On Tue, Mar 31, 2015 at 3:19 PM, Cheng Lian lian.cs@gmail.com wrote:
Hi Xudong,
This is probably because of Parquet schema merging is turned on by
default. This is generally useful for Parquet files with different but
compatible schemas. But it needs to read metadata from all Parquet
part-files. This can be problematic when reading Parquet files with lots of
part-files, especially when the user doesn't need schema merging.
This issue is tracked by SPARK-6575, and here is a PR for it:
https://github.com/apache/spark/pull/5231. This PR adds a configuration
to disable schema merging by default when doing Hive metastore Parquet
table conversion.
Another workaround is to fallback to the old Parquet code by setting
spark.sql.parquet.useDataSourceApi to false.
Cheng
On 3/31/15 2:47 PM, Zheng, Xudong wrote:
Hi all,
We are using Parquet Hive table, and we are upgrading to Spark 1.3. But
we find that, just a simple COUNT(*) query will much slower (100x) than
Spark 1.2.
I find the most time spent on driver to get HDFS blocks. I find large
amount of get below logs printed:
15/03/30 23:03:43 DEBUG ProtobufRpcEngine: Call: getBlockLocations took
2097ms
15/03/30 23:03:43 DEBUG DFSClient: newInfo = LocatedBlocks{
fileLength=77153436
underConstruction=false
blocks=[LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948_1448275;
getBlockSize()=77153436; corrupt=false; offset=0;
locs=[10.152.116.172:50010, 10.152.116.169:50010, 10.153.125.184:50010]}]
lastLocatedBlock=LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948_1448275;
getBlockSize()=77153436; corrupt=false; offset=0;
Re: Parquet Hive table become very slow on 1.3?
Hey Xudong,
We had been digging this issue for a while, and believe PR 5339
http://github.com/apache/spark/pull/5339 and PR 5334
http://github.com/apache/spark/pull/5339 should fix this issue.
There two problems:
1. Normally we cache Parquet table metadata for better performance, but
when converting Hive metastore Hive tables, the cache is not used. Thus
heavy operations like schema discovery is done every time a metastore
Parquet table is converted.
2. With Parquet task side metadata reading (which is turned on by
default), we can actually skip the row group information in the footer.
However, we accidentally called a Parquet function which doesn't skip
row group information.
For your question about schema merging, Parquet allows different
part-files have different but compatible schemas. For example,
part-1.parquet has columns a and b, while part-2.parquet may has
columns a and c. In some cases, the summary files (_metadata and
_common_metadata) contains the merged schema (a, b, and c), but it's not
guaranteed. For example, when the user defined metadata stored different
part-files contain different values for the same key, Parquet simply
gives up writing summary files. That's why all part-files must be
touched to get a precise merged schema.
However, in scenarios where a centralized arbitrative schema is
available (e.g. Hive metastore schema, or the schema provided by user
via data source DDL), we don't need to do schema merging on driver side,
but defer it to executor side and each task only needs to reconcile
those part-files it needs to touch. This is also what the Parquet
developers did recently for parquet-hadoop
https://github.com/apache/incubator-parquet-mr/pull/45.
Cheng
On 3/31/15 11:49 PM, Zheng, Xudong wrote:
Thanks Cheng!
Set 'spark.sql.parquet.useDataSourceApi' to false resolves my issues,
but the PR 5231 seems not. Not sure any other things I did wrong ...
BTW, actually, we are very interested in the schema merging feature in
Spark 1.3, so both these two solution will disable this feature,
right? It seems that Parquet metadata is store in a file named
_metadata in the Parquet file folder (each folder is a partition as we
use partition table), why we need scan all Parquet part files? Is
there any other solutions could keep schema merging feature at the
same time? We are really like this feature :)
On Tue, Mar 31, 2015 at 3:19 PM, Cheng Lian lian.cs@gmail.com
mailto:lian.cs@gmail.com wrote:
Hi Xudong,
This is probably because of Parquet schema merging is turned on by
default. This is generally useful for Parquet files with different
but compatible schemas. But it needs to read metadata from all
Parquet part-files. This can be problematic when reading Parquet
files with lots of part-files, especially when the user doesn't
need schema merging.
This issue is tracked by SPARK-6575, and here is a PR for it:
https://github.com/apache/spark/pull/5231. This PR adds a
configuration to disable schema merging by default when doing Hive
metastore Parquet table conversion.
Another workaround is to fallback to the old Parquet code by
setting spark.sql.parquet.useDataSourceApi to false.
Cheng
On 3/31/15 2:47 PM, Zheng, Xudong wrote:
Hi all,
We are using Parquet Hive table, and we are upgrading to Spark
1.3. But we find that, just a simple COUNT(*) query will much
slower (100x) than Spark 1.2.
I find the most time spent on driver to get HDFS blocks. I find
large amount of get below logs printed:
15/03/30 23:03:43 DEBUG ProtobufRpcEngine: Call: getBlockLocations took
2097ms
15/03/30 23:03:43 DEBUG DFSClient: newInfo = LocatedBlocks{
fileLength=77153436
underConstruction=false
blocks=[LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948_1448275;
getBlockSize()=77153436; corrupt=false; offset=0; locs=[10.152.116.172:50010
http://10.152.116.172:50010,10.152.116.169:50010
http://10.152.116.169:50010, 10.153.125.184:50010]}]
lastLocatedBlock=LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948
tel:1075187948_1448275; getBlockSize()=77153436; corrupt=false; offset=0;
locs=[10.152.116.169:50010 http://10.152.116.169:50010,10.153.125.184:50010
http://10.153.125.184:50010,10.152.116.172:50010 http://10.152.116.172:50010]}
isLastBlockComplete=true}
15/03/30 23:03:43 DEBUG DFSClient: Connecting to datanode10.152.116.172:50010
http://10.152.116.172:50010
I compare the printed log with Spark 1.2, although the number of
getBlockLocations call is similar, but each such operation only
cost 20~30 ms (but it is 2000ms~3000ms now), and it didn't print
the detailed LocatedBlocks info.
Another finding is, if I read the Parquet file via scala code
form spark-shell as below, it looks fine, the computation will
return the result quick as before.
Parquet Hive table become very slow on 1.3?
Hi all,
We are using Parquet Hive table, and we are upgrading to Spark 1.3. But we
find that, just a simple COUNT(*) query will much slower (100x) than Spark
1.2.
I find the most time spent on driver to get HDFS blocks. I find large
amount of get below logs printed:
15/03/30 23:03:43 DEBUG ProtobufRpcEngine: Call: getBlockLocations took 2097ms
15/03/30 23:03:43 DEBUG DFSClient: newInfo = LocatedBlocks{
fileLength=77153436
underConstruction=false
blocks=[LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948_1448275;
getBlockSize()=77153436; corrupt=false; offset=0;
locs=[10.152.116.172:50010, 10.152.116.169:50010,
10.153.125.184:50010]}]
lastLocatedBlock=LocatedBlock{BP-1236294426-10.152.90.181-1425290838173:blk_1075187948_1448275;
getBlockSize()=77153436; corrupt=false; offset=0;
locs=[10.152.116.169:50010, 10.153.125.184:50010,
10.152.116.172:50010]}
isLastBlockComplete=true}
15/03/30 23:03:43 DEBUG DFSClient: Connecting to datanode 10.152.116.172:50010
I compare the printed log with Spark 1.2, although the number of
getBlockLocations call is similar, but each such operation only cost 20~30
ms (but it is 2000ms~3000ms now), and it didn't print the detailed
LocatedBlocks info.
Another finding is, if I read the Parquet file via scala code form
spark-shell as below, it looks fine, the computation will return the result
quick as before.
sqlContext.parquetFile(data/myparquettable)
Any idea about it? Thank you!
--
郑旭东
Zheng, Xudong
