spark-postgres A library for reading data from and transferring data to Postgres / Greenplum with Spark SQL and DataFrames, 10~100x faster.spark-func-extrasA library that brings excellent and useful functions from various modern database management systems to Apache Spark.
Hi Pankaj,Can you show your detail code and Job/Stage Info? Which Stage is slow?Pankaj Bhootra <pankajbhoo...@gmail.com> 于2021年3月10日周三 下午12:32写道：Hi,Could someone please revert on this?ThanksPankaj BhootraOn Sun, 7 Mar 2021, 01:22 Pankaj Bhootra, <pankajbhoo...@gmail.com> wrote:Hello Team
I am new to Spark and this question may be a possible duplicate of the issue highlighted here: https://issues.apache.org/jira/browse/SPARK-9347
We have a large dataset partitioned by calendar date, and within each date partition, we are storing the data as parquet files in 128 parts.
We are trying to run aggregation on this dataset for 366 dates at a time with Spark SQL on spark version 2.3.0, hence our Spark job is reading 366*128=46848 partitions, all of which are parquet files. There is currently no _metadata or _common_metadata file(s) available for this dataset.
The problem we are facing is that when we try to run spark.read.parquet on the above 46848 partitions, our data reads are extremely slow. It takes a long time to run even a simple map task (no shuffling) without any aggregation or group by.
I read through the above issue and I think I perhaps generally understand the ideas around _common_metadata file. But the above issue was raised for Spark 1.3.1 and for Spark 2.3.0, I have not found any documentation related to this metadata file so far.
I would like to clarify:
- What's the latest, best practice for reading large number of parquet files efficiently?
- Does this involve using any additional options with spark.read.parquet? How would that work?
- Are there other possible reasons for slow data reads apart from reading metadata for every part? We are basically trying to migrate our existing spark pipeline from using csv files to parquet, but from my hands-on so far, it seems that parquet's read time is slower than csv? This seems contradictory to popular opinion that parquet performs better in terms of both computation and storage?
ThanksPankaj Bhootra---------- Forwarded message ---------
From: Takeshi Yamamuro (Jira) <j...@apache.org>
Date: Sat, 6 Mar 2021, 20:02
Subject: [jira] [Commented] (SPARK-34648) Reading Parquet Files in Spark Extremely Slow for Large Number of Files?
To: <pankajbhoo...@gmail.com>
[ https://issues.apache.org/jira/browse/SPARK-34648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17296528#comment-17296528 ]
Takeshi Yamamuro commented on SPARK-34648:
------------------------------------------
Please use the mailing list (user@spark.apache.org) instead. This is not a right place to ask questions.
> Reading Parquet Files in Spark Extremely Slow for Large Number of Files?
> ------------------------------------------------------------------------
>
> Key: SPARK-34648
> URL: https://issues.apache.org/jira/browse/SPARK-34648
> Project: Spark
> Issue Type: Question
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Pankaj Bhootra
> Priority: Major
>
> Hello Team
> I am new to Spark and this question may be a possible duplicate of the issue highlighted here: https://issues.apache.org/jira/browse/SPARK-9347
> We have a large dataset partitioned by calendar date, and within each date partition, we are storing the data as *parquet* files in 128 parts.
> We are trying to run aggregation on this dataset for 366 dates at a time with Spark SQL on spark version 2.3.0, hence our Spark job is reading 366*128=46848 partitions, all of which are parquet files. There is currently no *_metadata* or *_common_metadata* file(s) available for this dataset.
> The problem we are facing is that when we try to run *spark.read.parquet* on the above 46848 partitions, our data reads are extremely slow. It takes a long time to run even a simple map task (no shuffling) without any aggregation or group by.
> I read through the above issue and I think I perhaps generally understand the ideas around *_common_metadata* file. But the above issue was raised for Spark 1.3.1 and for Spark 2.3.0, I have not found any documentation related to this metadata file so far.
> I would like to clarify:
> # What's the latest, best practice for reading large number of parquet files efficiently?
> # Does this involve using any additional options with spark.read.parquet? How would that work?
> # Are there other possible reasons for slow data reads apart from reading metadata for every part? We are basically trying to migrate our existing spark pipeline from using csv files to parquet, but from my hands-on so far, it seems that parquet's read time is slower than csv? This seems contradictory to popular opinion that parquet performs better in terms of both computation and storage?
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