Pyspark write to s3 slow. write and the write process is taking time to write 544 * 7.

Pyspark write to s3 slow Speed up pyspark parsing large nested Oct 22, 2018 · S3 as a destination of work is (a) slow to commit and (b) runs the risk of losing data on account of S3's eventual consistency. pyspark. access. The first step is to create a Jan 23, 2020 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand Jan 10, 2025 · I have a Glue job which has the below configuration and writes file to s3 using spark. writeTo (table: str) → pyspark. It would be awesome to see if it helped :) First, if you coalesce, as said @Lamanus in the comments, it means that you will reduce the number of partitions, hence also reduce the number of writer task, hence shuffle all data to 1 task. Size : 50 mb. Spark is basically in a docker container. repartition(1) Aug 24, 2020 · writing a pyspark dataframe to AWS - s3 from EC2 instance using pyspark code the time taken to complete write operation is longer than usual time 0 partitionBy taking too long while saving a dataset on S3 using Pyspark Aug 24, 2021 · I am running a pyspark glue job with 10 DPU, the data in s3 is around 45 GB files split into 6 . json(path)) display(df) The problem is that it is very slow. Sep 8, 2019 · I am currently trying to write a delta-lake parquet file to S3, which I replace with a MinIO locally. For example, to append or create or replace existing tables. S3AFileSystem There's some code in org. It's super slow and takes 20min to Aug 28, 2022 · Pyspark UDF. Cluster Databricks( Driver c5x. sortBy. mode('overwrite'). The following code shows how to write a DataFrame to a Delta Lake table in PySpark: df. The data size is about 200GB, and 80 million datas. read . Dec 5, 2017 · df. Oct 28, 2020 · I am trying to figure out which is the best way to write data to S3 using (Py)Spark. format(“delta”). 2. To write Parquet files to S3 using PySpark, you can use the `write. writeTo¶ DataFrame. So you are writing the data to S3 twice. parquet()" is run. _jsc. 5mb file. csv("name. summary-metadata a bit differently: Oct 14, 2022 · I'm facing same problem. This committer improves performance when writing Apache Parquet files to Amazon S3 using the EMR File System (EMRFS). parquet()` function. json) in s3 with millions of json files (each file is less than 10 KB). Magic committers writes data directly to the final S3 destination in parallel, instead writing files to a temporary location and then Jun 28, 2018 · A while back I was running a Spark ETL which pulled data from AWS S3 did some transformations and cleaning and wrote the transformed data back to AWS S3 in Parquet format. As per the use case, our api will query the parquet based on id. very simple. 19. Its 6 billion records so far and it will keep growing daily. You need to create a Spark DF rather than a Pandas DF and then write to s3 – Mar 12, 2025 · When incorrectly configured, Spark apps either slow down or crash. 0 - Reading previous. Mar 1, 2019 · The EMRFS S3-optimized committer is a new output committer available for use with Apache Spark jobs as of Amazon EMR 5. Configure delta to s3. Dec 4, 2020 · I have a folder (path = mnt/data/*. Magic committers writes data directly to the final S3 destination in parallel, instead writing files to a temporary location and then S3 writes are extremely slow that it takes over 10 hours to finish running. parquet()` method takes a path to an S3 bucket and prefix as its first two arguments. May 6, 2018 · I have my scala spark job to write in to s3 as parquet file. csv. To write Parquet to S3 with PySpark, you can use the `spark. memory values will help determine if the workload requires more or less memory. From my SQL Profiler and Activity Monitor it seems that glueContext. For other small dataframe, I can see the "s3_path" can be created when "write. The real problem is that your data set/computations are not large enough or significant enough to overcome the coordination overhead and latency introduced by using Spark Jul 27, 2023 · Spark performs lazy evaluation. A deep look into the spark. Using coalesce(16) generates 16 2. AWS Glue - Writing File Takes A Apr 12, 2023 · Use foreachPartition instead of write: The write method writes data sequentially, which can be slow for large datasets. DataFrameWriter. Spark write Parquet to S3 the last task takes forever. Oct 28, 2020 · I am trying to figure out which is the best way to write data to S3 using (Py)Spark. 2xlarge, Worker (2) same as driver ) Source : S3. csv() instead I get 142 stages with each taking around 22 seconds to complete adding up to over 50 minutes! Each stage writes between 11 and 19 MB result. Jul 6, 2023 · PySpark write to parquet extremely slow. Oct 12, 2021 · The goal is to write some code to read these data, apply some logic on it using pandas/dask then upload them back to S3. Aug 15, 2020 · I'm using read API PySpark SQL to connect to MySQL instance and read data of each table for a schema and am writing the result dataframe to S3 using write API as a Apr 14, 2020 · The EMRFS S3-optimized committer improves write performance compared to FileOutputCommitter. option("inferSchema", False) . DataFrameWriterV2 [source] ¶ Create a write configuration builder for v2 sources. The EMRFS S3-optimized committer is a new output committer available for use with Apache Spark jobs as of Amazon EMR 5. sh file. I imagine what you get is a directory called Apr 27, 2017 · Suppose that df is a dataframe in Spark. partitionBy('id'). My main goal is writing millions of rows, but I am starting out small and to no avail - a 10-row dataframe wrote succesffully to S3, but 100 rows doesn't wr Jan 6, 2016 · Using pyspark isn't really going to be the problem - the Spark process is still written in Scala and how you interface with it doesn't affect the fact it has a Java backend. from pyspark. I have observed that even if data frame is empty still it takes same amount of time to write to s3. At this moment with pseudocode below, it takes around 8 hrs to read all the files and writing back to parquet is very very slow. Because the. Dec 10, 2022 · The most important learning here for me seems "simple test for what committer is live is to print the JSON _SUCCESS file". spark-redshift will always write the data to S3 and then use the Redshift copy function to write the data to the target table. Aug 10, 2021 · Sparks dataframe. That with the back and forth between s3 and spark leads to it being quite slow. sql. 4. For around 1200 records writing it too around 500 seconds alone for writing to s3. Create a SparkSession. I run the following code: df = (spark. I noticed that it takes really Jun 5, 2020 · The number of partitions of my output dataframe in pyspark is 300. . Aug 9, 2018 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand Dec 11, 2019 · spark. Mar 2, 2016 · I have had similar experiences when writing to Redshift both through Spark and directly. I have 700mb csv which conains over 6mln rows. Also will changing any of the config parameters before pyspark is invoked help solve the issue? Edits (a few clarifications): When I mean other operations were executed quickly, there was always an action after transformation, in my case they were row counts. Mar 3, 2022 · This outputs to the S3 bucket as several files as desired, but each part has a long file name such as: part-00019-tid-5505901395380134908-d8fa632e-bae4-4c7b-9f29-c34e9a344680-236-1-c000. If you have the expected data already available in s3, dataframe. My data frame looks like - id age gender salary item 1 32 M 30000 A 2 28 F 27532 B 3 39 M 32000 A 4 22 F 22000 C Following code is trying to take a bunch of files from one input S3 path and then write them into individual S3 folders with folder name as a date column in the input data. YARN container memory overhead can also cause Spark applications to slow down because it takes YARN longer to allocate larger pools of memory. In addition to slow S3 calls, the committer is making hundreds of thousands of S3 calls, which in some cases can be throttled by S3 (you can confirm this by enabling S3 logs and looking for 503 API responses). 3. Glue Config: worker type: G1 X; max number of workers: 10; glue version 5. from pyspark import Jul 1, 2016 · Hi there, I'm just getting started with Spark and I've got a moderately sized DataFrame created from collating CSVs in S3 (88 columns, 860k rows) that seems to be taking an unreasonable amount of time to insert (using SaveMode. 0. According to the official document:. Modified 1 year, 7 months ago. save(path) Where `df` is the DataFrame you want to write, and `path` is the path to the Delta Lake table. just read es and then write to hdfs. Each part file Pyspark creates has the . Note: For security reasons we are storing the AWS credentials in spark-env. PySpark S3 file read performance consideration. Instead of pre-defined functions, we can also use UDF(user-defined function ) . Especially with respect to s3. © Copyright . Optimising Spark read and write performance. Append) into Postgres. memory or spark. next. This builder is used to configure and execute write operations. Now let’s create a parquet file from PySpark DataFrame by calling the parquet() function of DataFrameWriter class. config setting p Jan 4, 2018 · I guess the to_csv method of the DF will be looking to write to a location in your local filesystem and failing because there is no such location locally. s3. Within the job it creates a number of dynamic frames that are then joined using spark. Ask Question Asked 1 year, 7 months ago. spark save taking lot of time. sparkContext. impl. But, for this large dataframe, nothing fodlers or files are created on "s3_path". Ensure that each job overwrite the particular partition it is writing to, in order to ensure idempotent Jan 19, 2022 · I've inherited some code that runs incredibly slowly on AWS Glue. Jul 6, 2023 · Spark will not write a single file but many files (depending on number of tasks, executors, spark partitions, etc. save. here is my code. You can use the `options()` method to configure the Parquet file format and the AWS S3 client. So you can do a few things to help mitigate/side step these issues. So to make Apr 1, 2022 · after creating the spark context use these lines to set the credentials spark. Hi I have a glue job running with PySpark. 0 Oct 1, 2020 · this is how I write to S3: rdd. Is it possible to write partitions into Dec 20, 2022 · Hudi seems to write the data without any problem, but it fails the indexing step which tries to collect a list of pairs (partition path, file id). anything wrong? PySpark extremely slow uploading to S3 Jun 7, 2019 · Ok, so spark is terrible at doing IO. The way to write df into a single CSV file is . Format : Parquet. option("multiline", True) . There are things you can do to speed this up. parquet() Jul 6, 2021 · However when I do df. first question: Its taking a lot of time to write data to Redshift from glue even tho I am Apr 21, 2023 · In general successive check pointing to S3 degrades performance. Write the results of my spark job to S3 in the form of partitioned Parquet files. executor. In your case you simply going through the data multiple times and it's very slow, plus you have a bottleneck in form of driver to which you do the . driver. UDFs are used to extend the framework’s functions and re-use Oct 28, 2016 · All spark dataframe writers (df. I also checked the s3 bucket, no folder created at "s3_path". I'm trying to save the combined dataframe in one parquet file in S3 but It shows me an error Dec 31, 2015 · I have a PySpark application that (obviously) loads and transforms data. csv") Wondering if anyone has run into the same issue. foreachPartition(lambda x: write_to_hdfs(x)) Here, write_to_hdfs is a function that writes the data to HDFS. df_meta_agg. 4xlarge with 4 nodes and all cores look not busy. For example, you can try something like this: df. RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. I need to write it straight to azure sql via jdbc. // 1. csv files. That is because S3 file paths are actually just keys to buckets. Oct 4, 2023 · The main idea here is that you can connect your local machine to your S3 file system using PySpark by adding your AWS keys into the spark session’s configuration with the configurations that I am trying to run spark locally to upload csv/parquet files to S3. We have a pyspark script which access an SQL Server instance through a VPN, perform a complex query and load ~6M tuples into S3 in parquet format. repartition(1). readwriter. In a naive implementation, inserting this DataFram pyspark. When you write a DataFrame to parquet file, it automatically preserves column names and their data types. I've started the spark shell like so (including the hadoop-aws package): Mar 17, 2023 · In addition to being unsafe, it can also be very slow. sql import SparkSession Mar 1, 2019 · November 2024: This post was reviewed and updated for accuracy. Not single word of Hadoop required in python or pyspark code. I want to persist the result RDD to S3, but the Spark-supplied . hadoop. Jan 20, 2022 · As explained earlier, the S3 rename operation is very slow (for large datasets). 0. Traceback (most recent call last): Jul 13, 2022 · I have 12 smaller parquet files which I successfully read them and combine them. S3 is made for large, slow, inexpensive storage. Jun 21, 2021 · I have a simple spark job which does 3 things: Read json data month-by-month from AWS S3 (Data is partitioned by date). So the more files you write to the same path the longer each successive lookup takes. So approximately I get 75K files summing to 11GB. Aug 30, 2021 · I'm struggling with one thing. Dec 10, 2019 · You could dump the data to a local CSV file first, and then use PostgreSQL's own import tools to import it - it depends on where the bottleneck is: is it slow to export from Pyspark or slow to import to Postgres, or something else? (That said, 14 minutes for 50 million rows doesn't seem that bad to me - what indexes are defined on the table?). Here are the steps involved in writing Parquet to S3 with PySpark: 1. Is there a way to expedite/optimize the s3 writes apart from increasing the number of nodes? Jun 28, 2018 · A while back I was running a Spark ETL which pulled data from AWS S3 did some transformations and cleaning and wrote the transformed data back to AWS S3 in Parquet format. write option copy the dataframe into temp directory and convert it to avro format and then use copy command of redshift. AbstractFileSystem. Dec 26, 2023 · A: To write a DataFrame to a Delta Lake table in PySpark, you can use the `write()` method. coalesce(1). Jan 23, 2022 · I work on a m4. You are using the field users_activity_id as a partition key and Hudi key, if the cardinality of this field is high, you will have a lot of partitions and then a very long list of pairs (partition, file_id), especially if this field is Hudi key which Dec 17, 2020 · The write stage stats from the UI: My worker stats: Quite strangely for the adequate partition size, the shuffle spill is huge: The questions I want to ask are the following: if the stage takes 1. You can try using the foreachPartition method to write data in parallel. init() import pyspark Jan 28, 2023 · Files written to S3. What you can try to do is cache the dataframe (and perform some action such as count on it to make sure it materializes) and then try to write again. Tables are read from a MySQL and Postgres db and then Glue is used to join them together to finally write another table back to Postgres. csv("out. 5 gb files and it didn't help much. To be more specific, perform read and write operations on AWS S3 using Apache Spark Python API PySpark. json(output_path) Spark will write 100 json files under the same path specified by 'output_path'. csv Jun 21, 2018 · Reading Millions of Small JSON Files from S3 Bucket in PySpark Very Slow. Is there a way to write this as a custom file name, preferably in the PySpark write function? Such as: part-00019-my-output. My Scenario I have a spark data frame in a AWS glue job with 4 million records I need to write it as a SINGLE parquet file in AWS s3 Current code file_spark_df. I can perfectly fine read/write standard parquet files to S3. Jan 4, 2020 · A simple code takes around 130 seconds to write s3-minio, while write to local disk takes 1 second only. After filtering it contains ~3mln. The volume of data Mar 12, 2025 · Why is Spark so slow? Find out what is slowing your Spark apps down—and how you can improve performance via some best practices for Spark optimization. save(path=s3_path, mode='overwrite', format='json') how ever all the tweaks with spark params from various online resouces , have only shaved minutes of the process . write and the write process is taking time to write 544 * 7. Earlier it used to take 30 min to complete the write operation for 1000 records, but now it is taking more than an hour. In my spark application, all stages to write this data to S3 are completed, but moving files from _temp folder are under progress. key", AWS_ACCESS_KEY_ID Feb 1, 2021 · The objective of this article is to build an understanding of basic Read and Write operations on Amazon Web Storage Service S3. parquet()` method. ___) don't write to a single file, but write one chunk per partition. Dec 25, 2019 · When I use df. Step 1: Define the Chunk Size; for Example — If the data frame contains more than Jun 10, 2021 · Configuration: Spark 3. Dec 1, 2016 · Spark does its stuff lazily. Able to read data from S3 with the PySpark but could not write the file to S3. csv but the actual CSV file will be called something like part-00000-af091215-57c0-45c4-a521-cd7d9afb5e54. ). 3h and workers do their work for 25 mins - does it imply that driver does the 50-min write to GCS? Aug 22, 2015 · I am trying to read a JSON file, from Amazon s3, to create a spark context and use it to process the data. May 15, 2019 · I have a pyspark data frame which I want to write in s3. df. impl org. s3a. This approach is the best practice and the most efficient way to write large numbers of records. I did Jun 24, 2022 · Persist will slow you down as your writing all the files to S3 with that. Mar 1, 2021 · I am trying to write a dataframe to S3. write might be less efficient when compared to using copy command on s3 path directly. parquet()) - I'll try to write my data in JSON & see if I can see the _SUCCESS May 24, 2019 · When we are writing a pyspark dataframe to s3 from EC2 instance using pyspark code the time taken to complete write operation is longer than usual time. Viewed 99 times 1 . FileSystem which looks up from a schema "s3" to an implementation class, loads it and instantiates it with the full URL. Below are code snippets - test1_df = test_df. The cluster i have has is 6 nodes with 4 cores each. Each parquet contains ~130 columns and 1 row and some of the files might have slight variations in schema. I've started the spark shell like so (including the hadoop-aws package): May 6, 2019 · I've about 2 million lines which are written on S3 in parquet files partitioned by date ('dt'). set("fs. from_options() is the weak spot. However, when I use the delta lake example. The number of partitioned csv files generated are also 70. Starting with Amazon EMR version 5. Setting up Spark session on Spark Standalone cluster; import findspark findspark. So I tried to set: fs. write. My script is taking more than two hours to make this upload to S3 (this is extremely slow) and it's running on Databricks in a cluster with: Mar 17, 2023 · In addition to being unsafe, it can also be very slow. A UDF is a basic user-defined function . Its taking too long to write the dynamic frame to s3. I'm searching for ideas on what to try to decrease run time by 50% Apr 22, 2021 · I have a AWS glue job (PySpark) that needs to load data from a centralized data lake of size 350GB+, prepare it and load into a s3 bucket partitioned by two columns. sh will trigger and load the credentials automatically to OS environment and will be available for Spark’s access. Spark 3. I don't currently see any _SUCCESS file in the S3 directory I am writing to - maybe that is because I am writing to parquet (with session. This means that it could be that the writing is very fast but the calculation in order to get to it is slow. Below is Sep 7, 2017 · Improving performance of PySpark with Dataframes and SQL. spark-env. I have column "id" (~250 unique values) which I use to write files with partitionBy. It seems I can't write delta_log/ to my MinIO. create_dynamic_frame. Currently when you are writing in spark it will use a whole executor to write the data SEQUENTIALLY. In this post, we run a performance […] I am trying to find the most efficient way to read them, uncompress and then write back in parquet format. csv") This will write the dataframe into a CSV file contained in a folder called name. collect. repartition(100). We also set parquet. Dec 10, 2021 · writing a pyspark dataframe to AWS - s3 from EC2 instance using pyspark code the time taken to complete write operation is longer than usual time 0 partitionBy taking too long while saving a dataset on S3 using Pyspark Jul 18, 2018 · Scenario. hadoopConfiguration(). Spark creates a job for this with one task. 1. If you want to migrate the database AWS has tools for that and it's worth looking into them. 16. Sep 11, 2023 · I Want to write a dataframe directly on s3 bucket by pyspark but dont want to use Hadoop any how. So putting files in docker path is also PITA. Aug 1, 2020 · Please consider the following as one of possible options. Overwrite the processed monthly data Jan 17, 2022 · I'm currently building an application with Apache Spark (pyspark), and I have the following use case: Run pyspark with local mode (using spark-submit local[*]). S3 takes longer to lookup files the more that you write to the same directory. fs. The `write. saveAsTextFile() function does not satisfy my requirements, Aug 27, 2020 · I am working on moving data from elasticsearch to hdfs. Jul 7, 2020 · Now when I want to save this dataframe to csv, it is taking a hell amount of time, the number of rows in this dataframe is only 70, and it takes around 10 minutes to write it to csv file. Unless you are using S3mper/S3Guard/EMR consistent EMRFS you shoudn't be using it as a direct destination of work. DataFrame. 0, you can use it with Spark’s built-in Parquet support. Steps to Write Parquet to S3 with PySpark. File count : 2000 ( too many small files as they are gettin ng dumped from kinesis stream with 1 min batch as we cannot have more latency 99) Sep 16, 2016 · In this mode new files should be generated with different names from already existing files, so spark lists files in s3(which is slow) every time. I came to this conclusion after noticing lack of open sessions on the target instance (RDS SQL Server) when Glue is obtaining dynamicFrame from the source (also RDS SQL Server). Awesome, we can now easily connect to S3 to read/write data. option("header", "true"). This function takes a Spark DataFrame as input and writes it to a Parquet file in S3. Do some minimal processing on the data. Spark newbie here. impl and fs. apache. parquet("s3://"+. Even if its so you can then move the files into S3. It seems I have no problem in reading from S3 bucket, but when I need to write it is really slow. parquet file extension. Feb 6, 2019 · I am running PySpark (on AWS Glue, if that matters). When I add a coalesce(1) before the write it's 1 stage that of course complains about lacking sufficient memory but also seems to take a horribly long time. It's not made to move data quickly. very slow. Jun 14, 2022 · Therefore, the vast and complex data must be divided into smaller chunks to overcome the S3 Slow Down exception. Mar 27, 2024 · Pyspark Write DataFrame to Parquet file format. enable. eeim uxhodd pzj fjpg omhmwb xhyhib idhvdl bvxhx wfssxbss sjwd ecienod cmxasnhv aapy wbqiy rbfb