If you've read the previous Spark with Python tutorials on this site, you know that Spark Transformation functions produce a DataFrame, DataSet or Resilient Distributed Dataset (RDD). For now I had to implement a loop for writing each partition out to a different subdir based on the partition columns but if the operation of partitionBy was available the. However the numbers won't be consecutive if the dataframe has more than 1 partition. What is the “Spark DataFrame”. However for DataFrame, repartition was introduced since Spark 1. When processing, Spark assigns one task for each partition and each worker threa. Having a nullable column of Doubles, I can use the following Scala code to. Selecting records can use Spark DataFrame method. Spark SQL - Column of Dataframe as a List - Databricks. A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. From performance perspective, it is highly recommended to use FILTER at the beginning so that subsequent operations handle less volume of data. Although, if any query occurs, feel free to ask in the comment section. Default Value: false; Added In: Hive 3. The spec argument must be given in the DSS partition spec format. Repartition a Spark DataFrame. sql("insert overwrite table table_name partition ('eventdate', 'hour', 'processtime')select * from temp_view") It preserves old partitions while (over)writing to only new partitions. mapPartitions() can be used as an alternative to map() & foreach(). Spark textFileStream dStream to DataFrame issues 0 Answers Predefined functions for Quality or Similarity Measures in GraphFrame 0 Answers Export data from Google Storage to S3 bucket using Spark on Databricks cluster,Export data from Google Storage to S3 using Spark on Databricks cluster 0 Answers. However for most of the cases, we need to down sample based on some hash function of a Key of the data. RDD operations like map, union, filter can operate on a single partition and map the data of that partition to resulting single partition. Hello All, In spark i am creating the custom partitions with Custom RDD, each partition will have different schema. Now, we will check the data and see how many partitions (s) has been created. Here is the example below which will give. In the following example, createDataFrame() takes a list of tuples containing names and ages, and a list of column names:. Spark RDD filter() function returns a new RDD, containing only the elements that meet a predicate. RDDs can be created from Hadoop input formats (such as HDFS files) or by transforming other RDDs. • The DataFrame API is likely to be more efficient, because. Spark DataFrames were introduced in early 2015, in Spark 1. The requirement is to load JSON. •However, stick with the DataFrame API, wherever possible. Writes out the source dataframe partitioned by the provided column. Partitioning means, the division of the large dataset. You can even use primary key of. where df is dataframe having the incremental data to be overwritten. How can we specify number of partitions while creating a Spark dataframe. To ensure that all requisite Phoenix / HBase platform dependencies are available on the classpath for the Spark executors and drivers, set both ‘spark. dplyr also supports non-standard evalution of its arguments. Filtering a row in Spark DataFrame based on Filtering a row in Spark DataFrame based on matching values from a list. You can also find that Spark SQL uses the following two families of joins: InnerLike with Inner and Cross. IOException: (null) entry in command string: null chmod 0644 C:\Users\NG005454\OneDrive - CCHellenic\Documents\Python_Exercise ew_territory_temporary\0_temporary\attempt_20200116102706_0080_m_000000_1193\part-00000-a8e4807b-ad43-4e16-9a00-5c7218423c6e-c000. When no explicit sort order is specified, "ascending nulls first" is assumed. Datasets are similar to RDDs, however, instead of using Java serialization or Kryo they use a specialized Encoder to serialize the objects for processing or transmitting over the network. If you continue browsing the site, you agree to the use of cookies on this website. registerTempTable("tempDfTable") SqlContext. partitions as number of partitions. The implementation will vary depending on the version of Spark and whether the DataFrame or Resilient Distributed Dataset APIs are used, but the concept is the same. For example, let's say I have a large file in HDFS that is 5 GB,. You might familiar with the following code There, you used orderBy to put records in order, and assumed groupBy will keep the same order within each group. As a result, we have seen all the SparkR DataFrame Operations. • Using RDD operations will often give you back an RDD, not a DataFrame. While it is quite efficient for several filters, we are working on several interesting projects to. selfJoinAutoResolveAmbiguity option enabled (which it is by default), join will automatically resolve ambiguous join conditions into ones that might make sense. Partitions in Spark won’t span across nodes though one node can contains more than one partitions. Setting the write partition is not allowed in Python recipes, where write is controlled by the Flow. where df is dataframe having the incremental data to be overwritten. Enter your email address to follow this blog and receive notifications of new posts by email. RDDは大量のデータを要素として保持する分散コレクションです。RDDは複数のマシンから構成されるクラスタ上での分散処理を前提として設計されており、内部的にはpartitionという塊に分割されています。. Spark operates on these elements in parallel. Narrow Operations. A Transformer reads a DataFrame and returns a new DataFrame with a specific transformation applied (e. Load spark dataframe into non existing hive table. - Explain RowNumber,Partition,Rank and DenseRank ?. If you continue browsing the site, you agree to the use of cookies on this website. DataFrame SortWithinPartitions (string column, params string[] columns); member this. com DataCamp Learn Python for Data Science Interactively. For Spark DataFrame, the filter can be applied by special method where and filter. If you do this with an RDD, Spark will execute this way. These posts would be better to read as the code here no longer works following changes to sparklyr. Now in the transformation step we need to get the schema and run some Dataframe SQL queries per partition, because each partition data has different schema. A DataFrame is a Dataset of Row objects and represents a table of data with rows and columns. • This means you can use normal RDD operations on DataFrames. What is the "Spark DataFrame". partitions as number of partitions. Spark RDD groupBy function returns an RDD of grouped items. It is supposed to give you a more pleasant experience while transitioning from the legacy RDD-based or DataFrame-based APIs you may have used in the earlier versions of Spark SQL or encourage migrating from Spark Core's RDD API to Spark SQL's Dataset API. In Spark, dataframe is actually a wrapper around RDDs, the basic data structure in Spark. In simple terms, RDD is a distribute collection. Apache spark groupByKey is a transformation operation hence its evaluation is lazy; It is a wide operation as it shuffles data from multiple partitions and create another RDD; This operation is costly as it doesn’t use combiner local to a partition to reduce the data transfer. Even though RDDs are a fundamental data structure in Spark, working with data in DataFrame is easier than RDD most of the time and so understanding of how to convert RDD to DataFrame is necessary. Not able to add reviewers for some reason :/ Can be reviewed now!. cannot construct expressions). Sql DataFrame. SPARK-18185 — Should fix INSERT OVERWRITE TABLE of Datasource tables with dynamic partitions; So, if you are using Spark 2. Any problems email [email protected] RDD is the fundamental data structure of Spark. The logic to download, and all the changes are prepared, but I am not sure what is the best way to make a loop around this. Introducing. Use filter() to return the rows but would like to partition on a. You can vote up the examples you like and your votes will be used in our system to produce more good examples. The entry point to programming Spark with the Dataset and DataFrame API. However the numbers won't be consecutive if the dataframe has more than 1 partition. This function is used with Window. Transpose Data in Spark DataFrame using PySpark. Scalable Partition Handling for Cloud-Native Architecture in Apache Spark 2. Spark Under The Hood : Partition. 4 of Window operations, you can finally port pretty much any relevant piece of Pandas' Dataframe computation to Apache Spark parallel computation framework using Spark SQL's Dataframe. • The DataFrame API is likely to be more efficient, because. When we are calling a DataFrame transformation, it actually becomes a set of RDD transformation underneath the hood. We can see that all "partitions" Spark are written one by one. but would like to partition on a particular column. Now, we will check the data and see how many partitions (s) has been created. Caused by: org. I am trying to create partitions programmatically from `DataFrame. Partitioning in Hive Requirement Suppose there is a source data, which is required to store in the hive partitioned table Load JSON Data in Hive non-partitioned table using Spark Requirement Suppose there is a source data which is in JSON format. iloc: Purely integer-location based indexing for selection by position. head ([n, npartitions, compute]) First n rows of the dataset: DataFrame. How to add new column in Spark Dataframe. The data source api at a high level is an api for turning data from various sources into spark dataframe and allows us to manage the structured data in any format. I want to remove some lines which doesn't match a string, but using filter is removing some contents from lines. IntelliJ; Introduction to Spark SQL; Datasource API; Dataframe; Load CSV data; Load JSON Data. It's really common in Big Data ad hoc analysis we need to down sample the data. /** * Merges multiple partitions of spark text file output into single file. public Microsoft. I don't know why in most of books, they start with RDD rather than Dataframe. Spark will look for all such opportunities and apply the pipelining where ever it is applicable. This is an example of how to write a Spark DataFrame by preserving the partitioning on gender and salary columns. have to read all of them in and filter. Output Ports The persisted Spark DataFrame/RDD. Basically the join operation will have n*m This entry was posted in DataFrame, Spark. For example, suppose you have a table that is partitioned by a, b, and c:. A Databricks table is a collection of structured data. class pyspark. cacheTable(“tableName”) or dataFrame. One way you can do this is to list all the files in each partition and delete them using an Apache Spark job. Python is revealed the Spark programming model to work with structured data by the Spark Python API which is. I am using Spark (in Databricks). Spark Overview Unified Analytics Engine Apache Spark. Calling cache() does not cause a DataFrame to be computed. As we are dealing with big data, those collections are big enough that they can not fit in one node. The second method provided by all APIs is coalesce which is much more performant than repartition because it does not shuffle data but only instructs Spark to read several existing partitions as one. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. You can use the following APIs to accomplish this. Greenplum Database distributes its table data across segments running on segment hosts. x: A spark_connection, ml_pipeline, or a tbl_spark. With the introduction in Spark 1. Repartition a Spark DataFrame. Remember, you already have SparkSession spark and people_df DataFrame available in your workspace. If the name of the partition field is in variable f and the known value of the field corresponding to training partition is in variable v: df = cxt. size() in Java/Scala and rdd. The UDF then returns a transformed Pandas dataframe which is combined with all of the other partitions and then translated. Filter records in pig. RDD的描述 Internally, each RDD is characterized by five main properties:A list of partitions A fu [2. 3 Dataframe features. One of the most popular features of Spark SQL is UDFs, or user-defined functions. Filter spark DataFrame on string contains - Wikitechy. Hope you like our explanation. Let's have some overview first then we'll understand this operation by some examples in Scala, Java and Python languages. Output Ports The persisted Spark DataFrame/RDD. take(1)) this take 1 element each partition in dataframe. apache spark sql and dataframe guide spark sql can convert an rdd of row object to a dataframe parquet") # create another dataframe in a new partition. The following examples show how to use org. partition_by: vector of column names used for partitioning, only supported for Spark 2. asked Jul 20, 2019 in Big Data Hadoop & Spark by Aarav Spark automatically monitors cache usage on each node and drops out old data partitions in a least-recently-used (LRU) fashion. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. mapPartitions() is called once for each Partition unlike map() & foreach() which is called for each element in the RDD. FxDataFrame's Arrow support means true zero copy exchange of data. Actually, Spark automatically monitors cache usage on each node and drops out old data partitions in a least-recently-used (LRU) fashion. A Dask DataFrame is a large parallel DataFrame composed of many smaller Pandas DataFrames, split along the index. I don't know why in most of books, they start with RDD rather than Dataframe. ) and/or Spark SQL. If None, similar to True the dataframe’s index(es) will be saved. partitions for Spark SQL or by calling repartition() Adding sequential IDs to a Spark Dataframe. If you must work with pandas api, you can just create a proper generator from pandas. To create a DataFrame, first create a SparkSession object, then use the object’s createDataFrame() function. That simply means pushing down the filter conditions to the early stage instead of applying it at the end. In the next post, we will see how to specify IN or NOT IN conditions in FILTER. I have created a hive table partitioned by country. Moreover, as mentioned in the comments, this is the case today but this code may break completely with further versions or spark and that would be very hard to debug. When the action is triggered after the result, new RDD is not formed like transformation. Conceptually, it is equivalent to relational tables with good optimizati. Resilient distributed datasets are Spark's main programming abstraction and RDDs are automatically parallelized across the cluster. Gets number of partitions of a Spark DataFrame. This class contains the basic operations available on all RDDs, such as map, filter, and persist. Requirement: In source data, you have user's. This article demonstrates a number of common Spark DataFrame functions using Python. 2) does not support nested JavaBeans and complex data types (such as List, Array). It can also handle Petabytes of data. For example, Suppose RDD contains first five natural numbers (1, 2, 3, 4, and 5) and the predicate is check for an even number. Now in the transformation step we need to get the schema and run some Dataframe SQL queries per partition, because each partition data has different schema. When processing, Spark assigns one task for each partition and each worker threads can only process one task at a time. Spark SQL is a Spark module for structured data processing. In order to filter data, according to the condition specified, we use the filter command. Filter records in pig. partitionBy() is a DataFrameWriter method that specifies if the data should be written to disk in folders. Partition pruning is an optimization technique to limit the number of partitions that are inspected by a query. Any problems email [email protected] This is an example of how to write a Spark DataFrame by preserving the partitioning on gender and salary columns. Create DataFrames Use filter() to return the rows I'd like to write out the DataFrames to Parquet, but would like to partition on a particular column. The dataframe we handle only has one "partition" and the size of it is about 200MB uncompressed (in memory). Spark dataframe add row number is very common requirement especially if you are working on ELT in Spark. Hello All, In spark i am creating the custom partitions with Custom RDD, each partition will have different schema. See [SPARK-6231] Join on two tables (generated from same one) is broken. Spark has moved to a dataframe API since version 2. 0 API Improvements: RDD, DataFrame, DataSet and SQL here. And we have provided running example of each functionality for better support. Christos - Iraklis Tsatsoulis June 23, 2015 Big Data, Spark 4 Comments [EDIT: Thanks to this post, the issue reported here has been resolved since Spark 1. sql(newQuery) //builtin function val dataframe = dataframe. its always a question for developers when to use Repartition and Coalesce over. 3 and coalesce was introduced since Spark 1. hadoop - spark dataframe write to hive partition Hive에 Spark 데이터 프레임을 동적 파티션 테이블로 저장 (4). Spark provides the Dataframe API, which is a very powerful API which enables the user to perform parallel and distrivuted structured data processing on the input data. Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. 5, test = 0. For example, to process credit card data, we want to perform the sampling consistently across all the…. A HiveContext SQL statement is used to perform an INSERT OVERWRITE using this Dataframe, which will overwrite the table for only the partitions contained in the Dataframe:. Delta Lake supports most of the options provided by Apache Spark DataFrame read and write APIs for performing batch reads and writes on tables. We can see also that all "partitions" spark are written one by one. Code review; Project management; Integrations; Actions; Packages; Security. Memory partitioning is often important independent of disk partitioning. SparkSQL the SQL query engine for Spark, uses an extension of this RDD called, DataFrame, formerly called a SchemaRDD. A DataFrame is a distributed collection of data organized into named columns. repartition(num,col("column1")) My main question is on what basis should I select the number of partitions to provide because my data size keeps changing. When I do an orderBy on a pyspark dataframe does it sort the data across all partitions (i. Partitions in Spark won't span across nodes though one node can contains more than one partitions. Both of them are actually changing the number of partitions where the data stored (as RDD). parallelize(Seq(("Databricks", 20000. Why Your Join is So Slow. Not able to add reviewers for some reason :/ Can be reviewed now!. mapPartitions() is called once for each Partition unlike map() & foreach() which is called for each element in the RDD. Spark has moved to a dataframe API since version 2. Memory partitioning is often important independent of disk partitioning. Returns the new DynamicFrame. The current Spark SQL version (Spark 1. Selecting records can use Spark DataFrame method. Incorrect data can appear when one dataframe is derived from the other with different filters or projections (parent-child dataframe with different set of filters/projections). December 15, 2016 by Eric Liang, Of course, you can still use native DataFrame APIs such as df. Each dataset in an RDD is divided into logical partitions, which may be transparently computed on different nodes of the cluster. Spark SQL is Apache Spark's module for A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. However, for some use cases, the repartition function doesn’t work in the way as required. After Spark installation, You can create RDDs and perform various transformations and actions like filter(), partitions(), cache(), count(), collect, etc. The number of partitions determine the file parts created when the dataframe is saved as file. filter("age is not null") Now we can map to the Person class and convert our DataFrame to a Dataset. Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it terminates. Spark distributes this partitioned data among the different nodes to perform distributed processing on the data. Also, allows the Spark to manage schema. From now on we can cache it, check its structure, list columns etc. To change the number of partitions in a DynamicFrame, you can first convert it into a DataFrame and then leverage Apache Spark's partitioning capabilities. Filtering Data. Through the reflection of the Bean to obtain the basic information, according to Bean information definition Schema. Filter with mulitpart can be only applied to the columns which are defined in the data frames not to the alias column and filter column should be mention in the two part name dataframe_name. Returns a new DataFrame that has exactly numPartitions partitions. So I thought to repartition the dataframe with a column and numOfPatitions. Apache Spark. Partitioning means, the division of the large dataset. selfJoinAutoResolveAmbiguity option enabled (which it is by default), join will automatically resolve ambiguous join conditions into ones that might make sense. Spark spawns method of the SQLContext reads a table from any jdbc datasource as a dataframe. This is the moment when you learn that sometimes relying on defaults may lead to poor performance. How to Improve Performance of Delta Lake MERGE INTO Queries Using Partition Pruning. coalesce() and repartition() change the memory partitions for a DataFrame. In my opinion, however, working with dataframes is easier than RDD most of the time. We know that Spark divides data into partitions and perform computations over these partitions. We even solved a machine learning problem from one of our past hackathons. Spark DataFrame expand on a lot of these concepts, allowing you to transfer that knowledge easily by understanding the simple syntax of Spark DataFrames. Ways to create DataFrame in Apache Spark [Examples with Code] Steps for creating DataFrames, SchemaRDD and performing operations using SparkSQL; How to filter DataFrame based on keys in Scala List using Spark UDF [Code Snippets] How to get latest record in Spark Dataframe; Common issues with Apache Spark; Comparison between Apache Spark and. When we talk about Dataframe’s partitions we are talking about how the data is distributed across all the machines on our cluster. DataNoon - Making Big Data and Analytics simple! All data processed by spark is stored in partitions. mapPartitions() is called once for each Partition unlike map() & foreach() which is called for each element in the RDD. I need to read compressed Avro file , and need each task to process fewer records , but allocate more tasks. See how Spark Dataframe FILTER/WHERE works:. The resulting Dataset is range partitioned. Spark DataFrame. A Databricks table is a collection of structured data. emptyRDD[String] // creates EmptyRDD[1] Creating empty RDD with partition. HOT QUESTIONS. It offers much tighter integration between relational and procedural processing, through declarative DataFrame APIs which integrates with Spark code. head ([n, npartitions, compute]) First n rows of the dataset: DataFrame. extraClassPath’ and ‘spark. As a result, we have seen all the SparkR DataFrame Operations. I'm loading a text file into dataframe using spark. These kind of operations which maps data from one to one partition are referred as Narrow operations. 2) does not support nested JavaBeans and complex data types (such as List, Array). Returns a new DataFrame with each partition sorted by the given expressions. A Databricks table is a collection of structured data. A Transformer reads a DataFrame and returns a new DataFrame with a specific transformation applied (e. Projection and filter pushdown improve query performance. In the second example it is the "partitionBy(). The default value for spark. head ([n, npartitions, compute]) First n rows of the dataset: DataFrame. Apache Spark is a fast and general-purpose cluster computing system. sql ("SELECT accNo, tranAmount FROM trans WHERE accNo like 'SB%' AND tranAmount > 0") # Register temporary table in the DataFrame for using it in SQL goodTransRecords. 明明学过那么多专业知识却不知怎么应用在工作中,明明知道这样做可以解决问题却无可奈何。 你不仅仅需要学习专业数学模型,更需要学习怎么应用数学的方法。. So they needs to be partitioned across nodes. SQLDataException: ORA-01861: literal does not match format string: Java source code (this code works fine for mysql & mssql databases) :. Mastering Spark [PART 16]: How to Check the Size of a Dataframe? 1 minute read. RDD的描述 Internally, each RDD is characterized by five main properties:A list of partitions A fu [2. In simple terms, RDD is a distribute collection. col!="" """) Filter a DataFrame column which contains null. RDD operations like map, union, filter can operate on a single partition and map the data of that partition to resulting single partition. In this blog, we will discuss the comparison between two of the datasets, Spark RDD vs DataFrame and learn detailed feature wise difference between RDD and dataframe in Spark. extraClassPath’ and ‘spark. Both of them are actually changing the number of partitions where the data stored (as RDD). createOrReplaceTempView ("goodtrans") # Show the first few records of the DataFrame goodTransRecords. Introduction to DataFrames - Scala. See GroupedData for all the available aggregate functions. 0 failed 1 times, most recent failure: Lost task 0. Also, allows the Spark to manage schema. More than a year later, Spark's DataFrame API provides a rich set of operations for data munging, SQL queries, and analytics. From performance perspective, it is highly recommended to use FILTER at the beginning so that subsequent operations handle less volume of data. I thought about filtering the partition boundary information and calculating the necessary information locally and join back on the original Dataframe after that, but I'd like to have an easier approach. dplyr also supports non-standard evalution of its arguments. collect() this return array number of elements equal number of partitions. Spark SQL and DataFrame Guide. Spark RDD, DataFrame and DataSet. RepartitionByRange(Column[]) RepartitionByRange(Column[]) RepartitionByRange(Column[]) Returns a new DataFrame partitioned by the given partitioning expressions, using spark. @srowen The reason I wanted to use partitionBy in the first place is for the subdirs created by the operation. How to add new column in Spark Dataframe. Actually, Spark automatically monitors cache usage on each node and drops out old data partitions in a least-recently-used (LRU) fashion. When using filters with DataFrames or the Python API, the underlying Mongo Connector code constructs an aggregation pipeline to filter the data in MongoDB before sending it to Spark. The requirement is how to get specific partition records in Spark using Scala. 200 by default. Now In this tutorial we have covered Spark SQL and DataFrame operation from different source like JSON, Text and CSV data files. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Before starting the comparison between Spark RDD vs DataFrame vs Dataset, let us see RDDs, DataFrame and Datasets in Spark: Spark RDD APIs - An RDD stands for Resilient Distributed Datasets. distributed import Client, LocalCluster lc = LocalCluster(processes=False, n_workers=4) client = Client(lc) channel1 = client. its always a question for developers when to use Repartition and Coalesce over. save()" that write directly to S3. SPARK-18185 — Should fix INSERT OVERWRITE TABLE of Datasource tables with dynamic partitions; So, if you are using Spark 2. In this exercise, you will filter the rows in the people_df DataFrame in which 'sex' is female and male and create two different datasets. In Scala and Java, the type information is extracted from the language’s type system (from JavaBeans and Scala case classes). Apache Spark SQL and data analysis - [Instructor] Now let's take a look at how we can use the Dataframe API to filter some of the rows in our Dataframe. So all rows in the table will be partitioned and. The resulting Dataset is range partitioned. Partitioning means, the division of the large dataset. Here’s How to Choose the Right One See Apache Spark 2. spark series As part of our spark tutorial series, we are going to explain spark concepts in very simple and crisp way. With narrow transformations, Spark will automatically perform an operation called pipelining on narrow dependencies, this means that if we specify multiple filters on DataFrames they’ll all be performed in-memory. What my requirement is to overwrite only those partitions present in df at the specified hdfs path. •DataFrames are built on top of the Spark RDD* API. By default Spark SQL uses spark. SPARK-18185 — Should fix INSERT OVERWRITE TABLE of Datasource tables with dynamic partitions; So, if you are using Spark 2. How can we specify number of partitions while creating a Spark dataframe. Having a good cheatsheet at hand can significantly speed up the development process. Partitioning datasets with a max number of files per partition Partitioning dataset with max rows per file Partitioning dataset with max rows per file pre Spark 2. public DataFrame filter Once called, it won't change even if you change any query planning related Spark SQL configurations (e. Apache spark groupByKey is a transformation operation hence its evaluation is lazy; It is a wide operation as it shuffles data from multiple partitions and create another RDD; This operation is costly as it doesn’t use combiner local to a partition to reduce the data transfer. You can call an action on it before adding the 2 records. Spark DataFrame. We can see also that all "partitions" spark are written one by one. partitions for Spark SQL or by calling repartition() Adding sequential IDs to a Spark Dataframe. mapPartitions(lambda x: some_function(x)) The result is an rdd of pandas. @srowen The reason I wanted to use partitionBy in the first place is for the subdirs created by the operation. You can even use primary key of.