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-00001.parquet has columns a and b, while part-00002.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; >> 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 >> >> >> > > > -- > 郑旭东 > Zheng, Xudong > > > -- 郑旭东 Zheng, Xudong
