Pyspark dataframe memory size reddit. Here's a brief description of each: cache(): This .

Pyspark dataframe memory size reddit Mar 27, 2024 · Similar to Python Pandas you can get the Size and Shape of the PySpark (Spark with Python) DataFrame by running count() action to get the number of rows on DataFrame and len(df. 0. This has been automated using a metadata table and a PySpark script. I have studied the concepts of Spark and practice few basic data frame, RDD and spark sql based questions. I want to learn Spark. Memory is a critical resource in Spark, used for caching data, executing tasks, and shuffling intermediate Reason i ask is bcz i'm used to pandas syntax. I do not see a single function that can do this. And finally use start your analysis on it. MEMORY_AND_DISK or MEMORY_ONLY? Is it diferent from RDD and Dataframe? Mastering Memory Management in PySpark: Optimizing Performance for Big Data Processing PySpark, the Python API for Apache Spark, is a powerful tool for processing large-scale datasets in a distributed computing environment. Nov 29, 2024 · Press enter or click to view image in full size In the world of data analysis and manipulation, the tools we choose significantly shape our workflows and outcomes. 200 * 128 / 1024 = 25GB. But if you need to combine the transformed partitions, you will need memory greater than the We would like to show you a description here but the site won’t allow us. The issue I am facing is that it is taking forever to write to the delta table. My source data on which this transformation happens is of 2GB size. I gave this talk at PyData NYC last week. Apr 10, 2025 · Reading large files in PySpark is a common challenge in data engineering. Hey r/apachespark, I'm having a frustrating issue with Apache Spark and could really use some advice from this knowledgeable community. As you can't this using Spark SQL, does this mean the parquet file doesn't get converted into memory and you are, in fact, hitting the file directly? Spark Memory Management: Optimize Performance with Efficient Resource Allocation Apache Spark’s ability to process massive datasets in a distributed environment makes it a cornerstone of big data applications, but its performance heavily depends on how effectively it manages memory. I’m trying to use applyinpandas to do so, but I keep coming across some types of memory issues or the cluster just restarting after a few hours. Sometimes it’s also helpful to know the size if you are broadcasting the DataFrame to do broadcast join. Aug 3, 2018 · For datasets that are greater than size of RAM 100s of gigabytes I have seen tutorials where they use spark to filter out based on rules and generate a dataframe that fits in memory eventually there is always data that resides entirely in memory but i want to know how to work with big data set and perform exploratory data analysis When I was learning about Spark with Scala I read that Datasets are a very powerful feature as they allow for compile-time type checking, as well as taking advantage of functional programming when performing transformations. Examples Allows you to perform data engineering related tasks using Python & dataframes w/o using SQL in your code. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. hello, i used snowpark in order to read data from snowflake with python as a pandas dataframe, you can find bellow the source code, but i faced some issues maybe related to the large size of data (204 GB), any suggestions for a solution because when i'm trying to convert the dataframe to pandas it just keeps loading with no result till it shows In general, spark operations on spark data frames can be parallelised across worker nodes, and is usually natively handled well by spark (i. But most sources I read mentioned that they are also slower than DataFrames, although according to this article from 2021 the performance gap between these two is Oct 5, 2024 · Finding the Size of a DataFrame There are several ways to find the size of a DataFrame in PySpark. I don’t see any particular technical objections to a pandas-like thing that can work on-disk, ie without loading the entire dataset in-memory - which of course is exactly what these OLAP databases allow you to do. We would like to show you a description here but the site won’t allow us. Ex: If you have Region column in the raw data set, split the raw data based on region in input do explicit parallel processing to get most of your cluster. Trying to process 12 Billion Rows. collect() [func(row) for row in rows] # 50secs In my assumption, if I fully utilize all cores in the cluster, that would give me a runtime of roughly: total_cores = n_nodes * (n_core_per_node - 1) total_cores = 5 * 15 = 75 Pandas is not support to read over memory size dataset (the github replied me) , so my benchmark test moved to use DuckDB. Jun 19, 2024 · Handling large volumes of data efficiently is crucial in big data processing. ) that allow To cache a DataFrame in PySpark, you would use the cache () method. Are there any Python packages that can work with big data? Currently the data is stored in SQL. When i repartition the dataframe and count the number of partitions, it takes over an hour to print out the number of partitions as well. Proper partitioning ensures that your data is distributed evenly across the cluster, which can significantly Apr 2, 2025 · 📑 Table of Contents 🔍 Introduction ⚠️ Understanding the Challenges of Large-Scale Data Processing 💾 Memory Limitations 💽 Disk I/O Bottlenecks 🌐 Network Overhead 🧩 Partitioning Issues ⚙️ Cluster Configuration for Massive Datasets 🖥️ Executor Memory & Cores 🎮 Driver Memory Settings ⚖️ Dynamic vs. DataFrame. I'm running this on a company VM which has 16GB RAM I believe. Then, you can calculate the size of each column based on its data type. However, the same DataFrames process perfectly fine when using Spark 2. It was fun working with devs from various projects (Dask, Arrow, Polars, Spark) in… I want to write one large sized dataframe with repartition, so I want to calculate number of repartition for my source dataframe. May 6, 2016 · How to determine a dataframe size? Right now I estimate the real size of a dataframe as follows: headers_size = key for key in df. PySpark and Pandas are two Mar 27, 2025 · Processing large datasets efficiently is critical for modern data-driven businesses, whether for analytics, machine learning, or real-time processing. I'm working with large, deeply nested DataFrames, and when I try to apply the withColumn method in Spark 3. If you're using PySpark dataframe, then something is wrong with your distributed compute setup. functions. 110 votes, 18 comments. Is the image_data dataframe computed from any join operation? If so, try to force a broadcast join there. numberofpartition = {size of dataframe/default_blocksize} How to Here is how I would approach this. For me working in pandas is easier bc i remember many commands to manage dataframes and is more manipulable… but since what size of data, or rows (or whatever) is better to use pyspark over pandas? Jul 23, 2024 · Spark dataframe almost has 1M S-prefixed columns. createOrReplaceTempView ('df_view') Get data from S3 with spark. size # pyspark. This method stores the DataFrame in memory, allowing for quicker access on subsequent actions that utilize this DataFrame. 0 spark version. If the former (pandas dataframe), not much you can do. Otherwise return the number of rows times number of columns if DataFrame. I did so by creating chunks of the dataframe each of size determined by categorizing the data. Im assuming the data being returned is being crammed into a pandas dataframe, which is a single machine, in-memory data structure, meaning it is limited by the available RAM of your computer. It is widely used in data analysis, machine learning and real-time processing. You can think of a DataFrame like a spreadsheet, a SQL table, or a dictionary of series objects. Basic solution is to merge your Depending on your data you can also process it into a parquet file which should have a smaller memory footprint (though I’m not entirely sure about what happens when that parquet is loaded into a DataFrame so ymmv) Jul 18, 2025 · PySpark is the Python API for Apache Spark, designed for big data processing and analytics. Spark vs. + get the data cleaned and store it in parquet. PySpark, which is Spark’s Python interface, is popular among data engineers and scientists who work with big datasets. My benchmark test need alternative software to compare my code project. size(col) [source] # Collection function: returns the length of the array or map stored in the column. Given our cluster's capacity, I'm considering whether we can enhance performance using multithreading or multiprocessing. 1GB to 100 GB If the data file is in the range of 1GB to 100 GB, there are 3 options: Use parameter “chunksize” to load the file into Pandas dataframe Import data into Dask dataframe Ingest data into PySpark dataframe > 100GB What if the dataset Dec 9, 2023 · Discover how to use SizeEstimator in PySpark to estimate DataFrame size. But more generally when you want the workload to complete faster by running on multiple machines. Most of these are well known, such as use of … Mar 9, 2023 · Bookmark this cheat sheet on PySpark DataFrames. most articles suggest to use dataframes as much as possible, partly due to their ease of readability and the auto optimizer. - The cluster has roughly 2000 Cores and 2TB of memory Are you using the cluster optimally? Can you post the number of executors and number of cores per executor that you're using? Your data size is large and you are joining large data sets. In what cases will spark driver die due to OOM - like df. But these transformations takes a lot of time even with a cluster size of 64 GB, 8 cores with 10 executors. executor. pandas. limit(1000). I am trying to find out the size/shape of a DataFrame in PySpark. pyspark. Also, given the problem statement for the given dataset size, how would you approach the problem ? Thanks much in advance! All spark dataframe operations are immutable and it returns a new object instead of changing an existing one. -> create 's3_view' Do a 51 votes, 52 comments. When you persist a dataset, each node stores its partitioned data in memory and reuses them in other actions on that dataset. Feb 18, 2023 · Being a PySpark developer for quite some time, there are situations where I would have really appreciated a method to estimate the memory consumption of a DataFrame. I've tried to adapt all UDFs into native dataframe transformations and I have applied a watermark of 10 minutes with tumbling windows of 5 minutes so data should be dropped after the watermark expires. This can be useful to get a sense of the overall size of the dataset. I am applying some transformations on the dataframe in the form of UDFs and filter on some StructType column. Every example I found and also the PySpark documentation would suggest that this code should replace all 'null' values with the value found in the row above, but it simply doesn't do anything when executed. 🎯 How can we cache a dataframe? 🎯 What are the different storage levels that we can use? 🎯 I'll also share some tips to develop the vital instinct of caching in the right places. Is the performance difference minimal i. In PySpark, data partitioning refers to the process of dividing a large dataset into smaller chunks or partitions, which can be processed concurrently. g. Better to use pyspark over pandas. collect () that is too big, broadcast join. . There's is a minimal file system block size on PC it is 4-8 K, Hadoop default 64-128 M. I would like at my data coming in after any narrow transformations (filters and such) and pushdown predicates and pushdowns. Finally, I would merge that dataframe back to the original one, with a final size of 10000000 by 128. Under the hood, I get that in PySpark the parquet file is converted into a DataFrame which is an object in memory. I’m pretty sceptical about people suggesting distributed solutions like Spark/Dask for larger than memory data. Return the number of rows if Series. Can you list some important to cover / good to practice spark related questions for a DE interview? I have heard there are a lot of questions around Spark optimizations. For each dataframe, I would get the size MB, GB, whatever. 100K files. size # Return an int representing the number of elements in this object. Nov 28, 2023 · @William_Scardua estimating the size of a PySpark DataFrame in bytes can be achieved using the dtypes and storageLevel attributes. 💾 Caching is a super important feature in Spark, it remains to be seen how and when to use it knowing that a bad usage may lead to sever performance issues. I have below config for my glue job: Glue version : 4 Worker Type : G8X Mar 27, 2024 · Sometimes we may require to know or calculate the size of the Spark Dataframe or RDD that we are processing, knowing the size we can either improve the Spark job performance or implement better application logic or even resolve the out-of-memory issues. I've tried modin and pyspark, all with no luck. May 6, 2024 · Using persist() method, PySpark provides an optimization mechanism to store the intermediate computation of a PySpark DataFrame so they can be reused in subsequent actions. In Python, I can do this: data. Mar 27, 2024 · Spark Performance tuning is a process to improve the performance of the Spark and PySpark applications by adjusting and optimizing system resources (CPU cores and memory), tuning some configurations, and following some framework guidelines and best practices. Then I cleaned the data at individual chunk level which was much much faster and worked like a charm Mar 27, 2024 · Sometimes we may require to know or calculate the size of the Spark Dataframe or RDD that we are processing, knowing the size we can either improve the Spark job performance or implement better application logic or even resolve the out-of-memory issues. PySpark, the Python API for Apache Spark, provides a scalable, distributed framework capable of handling datasets ranging from 100GB to 1TB (and beyond) with ease. Apr 13, 2025 · As data keeps growing in many industries, Apache Spark is one of the best tools for processing large amounts of data. Maybe have a look at pyspark. Learn best practices, limitations, and performance optimisation techniques for those working with Apache Spark. For instance, let's assume I'm working with ps_df which is a pyspark. e no point of learning pyspark dataframe? Couldn't find much on google, wondering if any of you guys have benchmarked how they compare. ? My Production system is running on &lt; 3. My team uses Azure Synapse and runs PySpark (Python) notebooks to transform the data. Pandas Dataframes Hi everyone, I'm relatively new to the field of data engineering as well as the Azure platform. Static Allocation 🔢 Parallelism & Partition Tuning 📊 . The current process loads the data tables as spark Dataframes, and keeps them as spark dataframes throughout the process. You can try to collect the data sample and run local memory profiler. sql dataframe and ps_pandas_df which is a pyspark. However, its performance heavily depends on efficient memory management, as big data workloads can strain system resources. I can’t give any recommendations further with knowing data and business logic. lang. In what scenarios would one use RDD methods over dataframes today? Are there any benefits to using RDDs anymore beyond understanding spark? Nov 21, 2024 · In Pyspark, How to find dataframe size ( Approx. I’ve played around with pyspark before but I think there’s a df size threshold below which pandas is faster and above which pyspark is faster? Just not sure what this threshold is and whether anyone has tried finding out or have any heuristics. I'm newish to Databricks and Pyspark but not new to analytics and Python + Pandas. Multiply the number of elements in each column by the size of its data type and sum these values across all columns to Hey thanks for the detailed reply! Most of the issues I encounter are memory related and I’m more concerned with time taken than resources used, though granted I’m not working on an unlimited budget so both are variables I need to take into account. format ("csv") etc. Now I need to build some workflows. rows = df. row count : 300 million records) through any available methods in Pyspark. first (). I guess you spilt the data and run two or more jobs parallel and combine later. Here's a brief description of each: cache(): This Pandas requires memory about 5 times the size of data on disk. sql. So my assumption is that, it will create a new dataframe. shape() Is there a similar function in PySpark? Th I've got a relatively big dataset (8 GB) and pandas crashes when trying to load it into a dataframe. Current test I focus on many small files e. I've tried multiple approaches What is the default storage level of a DataFrame in Spark? StorageLevel. Can you point out few important topics or techniques to cover that? Any link to blog or article would also help. Feb 21, 2023 · In PySpark, cache() and persist() are methods used to improve the performance of Spark jobs by storing intermediate results in memory or on disk. Backdrop: I am using pyspark on AWS EMR (22G Mem available, 8 VCores). Big fs block size is feature, not a bug. Both the UDF changes and lowering the watermark/window time to 2 and 1 minute respectively have a very low improvement on memory consumption. asDict () rows_size = df. From reading you guys on Reddit, a lot of the benefit seems to be loading of files selectively, if I already have the data in memory (because it's being provided by another library that I have little control over) is there a significant advantage for using polars over pandas to handle/transform that data? Hi guys, just wondering if some of the more experienced DEs have an answer to this. Apr 19, 2024 · Performance Tuning for Spark, PySpark, and SparkSQL Coding Best Practices The first pillar of good performance is to follow coding best practices. Is that possible? If so, how? update inspired by the link in @zero323's comment, I tried to delete and recreate the context in PySpark: Anyway, I found snowpark useful for connecting to snowflake, getting your data, storing that data as a pandas dataframe (which defeats the purpose of snowpark), doing work in pandas/python, then using snowpark to move the resulting data and tables back to snowflake if needed. Snowpark dataframe functions are very similar to PySpark where 80-90% of your code will remain same with little need for change if you decide to switch. Data size would come down to few 100MBs, repartition on a colum which you feel would be a always on filter when reading, could be country, year Then use pyspark or pandas to read the file and process it. This is an important aspect of distributed computing, as it allows large datasets to be processed more efficiently by dividing the workload among multiple Using PySpark and its internal modules should solve a good chunk of your larger query processing and loads tbh At the most basic level I use pyspark. Non spark operations are usually all handled by the driver node, and so can only achieve parallelisation within the resources of the driver node. There seems to be no straightforward way to find this. dtypes. It kills any big data tool performance. It lets Python developers use Spark's powerful distributed computing to efficiently process large datasets across clusters. e partitioned data frames, native pyspark/sql operations). So I was running a: 5 node cluster with 16cores each in Google DataProc Let say that applying a simple function across 1000 rows finishes at 50secs. When you’re working with a 100 GB file, default configurations can lead to out-of-memory errors, slow execution, or even Faster transformations on a large dataframe I have a dataframe of 42 GiB, having close to 500 M rows. pandas dataframe: Example 1 - Describing Dataset This section introduces the most fundamental data structure in PySpark: the DataFrame. Spark divides the dataset into many partitions. sql fairly frequently and within that a lot of your work can be achieved using the DataFrame, functions and types classes What do people in this sub think about SQL vs Dataframes (like pandas, polars or pyspark) for building ETL/ELT jobs? Personally I have always preferred Dataframes because of A much richer API for more complex operations Ability to define reusable functions Code modularity Flexibility in terms of compute and storage Standardized code formatting Code simply feels cleaner, simpler and more Aug 22, 2019 · Pandas or Dask or PySpark < 1GB If the size of a dataset is less than 1 GB, Pandas would be the best choice with no concern about the performance. read. However, it's worth noting that the actual caching occurs only when an action is first called on the How do you avoid memory leaks in Spark/Pyspark for multiple dataframe edits and loops? There are 2 scenarios that I feel cause memory leaks that I struggle to know how to avoid. ( I am using default settings for memory and executors, new to spark and for lack of better words, dont know how to fix them optimally) What my job does: Reads two dataframes, crossjoins and writes the output. A simple way to estimate the memory consumption of PySpark DataFrames by programmatically accessing the optimised plan information… Nov 23, 2023 · How to estimate a PySpark DataFrame size? Sometimes it is an important question, how much memory does our DataFrame use? And there is no easy answer if you are working with PySpark. You can estimate the size of the data in the source (for example, in parquet file). Jul 1, 2025 · Learn how Spark DataFrames simplify structured data analysis in PySpark with schemas, transformations, aggregations, and visualizations. Apache Spark DataFrames support a rich set of APIs (select columns, filter, join, aggregate, etc. map (lambda row: len (value for key, Feb 24, 2025 · Vectorized operations in Pandas improve performance but don’t scale beyond memory limits. memory property, in PySpark, at runtime. The idea is b Jun 11, 2021 · There's not one answer, but generally, you certainly need Spark when you can't fit the data on one machine in memory, as is usually required for non-distributed implementations. I don't know what it is for s3/dbfs/ but it isn't zero. I have tried a bunch of methods. 🔗 Spark Caching, when and how? 📖 Enjoy How to find size of a dataframe using pyspark? I am trying to arrive at the correct number of partitions for my dataframe and for that I need to find the size of my df. Oct 31, 2024 · Efficient data partitioning is a critical aspect of optimizing PySpark performance. Are there certain DOs and DON'T when handling Pyspark dataframes and views? At a high level, I'm doing something like this: Read tables from Snowflake to df -> df. OutOfMemoryError: Java heap space I'd like to increase the memory available to Spark by modifying the spark. I use Go, plan to develope Python bingings. So create 1000 single line files, the system need to allocate 128000 M of memory. 4. It contains all the information you’ll need on dataframe functionality. I have recently started working with pyspark and need advice on how to optimize spark job performance when processing large amounts of data . functions to see if you can find something there (see here). Totally get this isn’t a one size fits all - the script I was working on pulls data from around 100 diff tables in a loop and sizes and Exception in thread "dag-scheduler-event-loop" java. It can read both billion-row csv and parquet. I’ve tried repartitioning, changing the schema to all nullable string types, and splitting the dataframe up into 4-10 equal sizes to append to the table. What is a size of a broadcast join dataframe limit - what circumstances can you increase it? What are some techniques of dealing with skewed joins? What is a broadcast variable? Should another cluster type be used for this use-case ? Should this be refactored or should Pandas dataframe usage be removed or any other pointer would be really helpful. I append… Suggestion 3: can you use the DataFrame API ? DataFrame API operations are generally faster and better than a hand-coded solution. PySpark, an interface for Apache Spark in Python, offers… Feb 4, 2025 · My Journey with PySpark: 10 Optimization Tricks Hey folks, I’m not a tech guru or anything fancy like that — just a data engineer who’s spent a few too many late nights wrestling with PySpark … Jul 23, 2025 · In this article, we are going to learn data partitioning using PySpark in Python. However, as the size of jobs increases, there can be problems with performance and reliability. columns()) to get the number of columns. First, you can retrieve the data types of the DataFrame using df. cache (). ( Yes, crossjoin is costly but thats what my requirement is, map every row to Assumming "df" is a PySpark dataframe, "col" is the column to impute and "ref_col" is the column to sort by. With the default shuffle size of 200, we can handle 25GB with the default spark partition bytes (128MB). This lazy execution allows Spark to optimize transformations before execution. size # property DataFrame. So 16 GB system memory can handle a 3 GB dataset. What would be some ways to improve performance for data transformations when working with spark dataframes? Any tips would be greatly appreciated , thanks! I have a dataframe, about 23M rows and 15-20 columns that I want to make some transformations on via pandas. 1, the driver runs out of memory and fails. One common approach is to use the count() method, which returns the number of rows in the DataFrame. I don't know if it's relevent since I have not seen your data but that's a general recommendation I do from my experience. I am trying to pull data from an API endpoint which gives out 50 records per call and has 30 million rows in total. In this guide, we’ll explore best practices, optimization techniques, and step Is there a guideline on how to select the most optimal number of partitions and buckets for my dataframe? My initial dataset is about 200GB (billions of rows, more than 30 billion rows), the “id” field in my data repeats and represents a set of events, each grouping of id’s has varying frequencies resulting in data skew. It processes the smaller partitions, so memory will not be a limiting factor. Large number of small files is not a Spark problem. Use something like a sorted heap of "next value" from each input, then keep popping the min & adding next value from that input, until all inputs & the heap are empty. I understand there are memory optimizations / memory overhead involved, but after performing these tests I don't see how SizeEstimator can be used to get a sufficiently good estimate of the dataframe size (and consequently of the partition size, or resulting Parquet file sizes). The script reads the table names from the metadata table, stores them in a list, and iterates through each table to perform the upsert operations. The syntax is straightforward: DataFrame. A terabyte or more is hard to put into memory, sometimes less. Cache Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerhouse for big data processing, and the cache operation is a key feature that lets you turbocharge your workflow by keeping a DataFrame in memory. This is from a Wes McKinney talk (sorry, don't have the link). Understanding and optimizing memory management The last step takes a trivial amount of memory (just whatever you use for buffering), runs in linear time, and can emit files at whatever size you wish as you proceed with the sort. jqjww upl mamtm xbg zxcokk dpks lvhfs rzznlk sunxg rthzo eabg hkerbfm pfzyz lxudga akbv