The example provided here is also available at Github repository for reference. If you are looking for PySpark, I would still recommend reading through this article as it would give you an Idea on Parquet usage.
Apache Parquet is a columnar file format that provides optimizations to speed up queries and is a far more efficient file format than CSV or JSON, supported by many data processing systems. Below are some advantages of storing data in a parquet format. Below are some of the advantages of using Apache Parquet.
Note that toDF function on sequence object is available only when you import implicits using spark. This complete spark parquet example is available at Github repository for reference. Using spark. Writing Spark DataFrame to Parquet format preserves the column names and data types, and all columns are automatically converted to be nullable for compatibility reasons.
Notice that all part files Spark creates has parquet extension. Similar to write, DataFrameReader provides parquet function spark. In this example snippet, we are reading data from an apache parquet file we have written before. This temporary table would be available until the SparkContext present.
Above predicate on spark parquet file does the file scan which is performance bottleneck like table scan on a traditional database. We should use partitioning in order to improve performance.
Partitioning is a feature of many databases and data processing frameworks and it is key to make jobs work at scale. We can do a parquet file partition using spark partitionBy function. Parquet Partition creates a folder hierarchy for each spark partition; we have mentioned the first partition as gender followed by salary hence, it creates a salary folder inside the gender folder.
This is an example of how to write a Spark DataFrame by preserving the partitioning on gender and salary columns. The execution of this query is significantly faster than the query without partition. It filters the data first on gender and then applies filters on salary. You have learned how to read a write an apache parquet data files in Spark and also learned how to improve the performance by using partition and filtering data with a partition key and finally appending to and overwriting existing parquet files.In my previous post about Data Partitioning in Spark PySpark In-depth WalkthroughI mentioned how to repartition data frames in Spark using repartition or coalesce functions.
Partitioner class is used to partition data based on keys. It accepts two parameters numPartitions and partitionFunc to initiate as the following code shows:. The first parameter defines the number of partitions while the second parameter defines the partition function. You may expect that each partition includes data for each Country but that is not the case. Because repartition function by default uses hash partitioning. For different country code, it may be allocated into the same partition number.
The output looks like the following: This output is consistent with the previous one as record ID 1,4,7,10 are allocated to one partition while the others are allocated to another question. There is also one partition with empty content as no records are allocated to that partition. Well, the first thing we can try is to increase the partition number. In this way, the chance for allocating each different value to different partition is higher. At the moment in PySpark my Spark version is 2.
So we can only use this function with RDD class. Through this customised partitioning function, we guarantee each different country code gets a unique deterministic hash number. Now if we change the number of partitions to 2, both US and CN records will be allocated to one partition because:.
For the above partitioned data frame 2 partitionsif we then write the dataframe to file system, how many sharded files will be generated? The answer is 2 as there are two partitions. However, if we change the last line of code to the follow: df. Then three folders will be created with one file in each. The partition number for CN and US folders will be the same since the data is from the same partition. CSV is a common format used when extracting and exchanging data between systems and platforms.
However there are a few options you need to pay attention to especially if you source file: Has records ac Schema evolution is supported by many frameworks or data serialization systems such as Avro, Orc, Protocol Buffer and Parquet. With schema evolution, one set of data can be stored in multiple files with different but compatible schema.
It is an important tool to do statistics. Most Databases support Window functions. Spark from version 1. There are hundreds of general spark functions in which Aggregate Functions and Window Functions categories are related to this case. Spark will throw out an exception when running it.
The values are only from unboundedPreceding until currentRow. The following example keeps the top 2 employees salary wise, others have to go. On the sample dataset, Wilma and Maja have the same salary. Maja has to go according to order, unfortunately. The real values we get are depending on the order.
Notice from the output, the first row in a window with lag will have value nulland the last row in a window with lead will have value null. We can calculate the difference with lead and lag compare the currentRow. We can replace the value null after getting the difference. Or like this example, using when to calculate the difference, fill in a literal value, e. We can, after calculating the difference, find some outliers which have a huge salary gap.
The following example takes employees whose salary is double to the next employee. Running Total means adding everything up until the currentRow. The parameters value can be Window. Or a value relative to Window. The difference compares to rowsBetween is that it compare with value of the current row. Here is an example use directly after Window.Focus in this lecture is on Spark constructs that can make your programs more efficient.
In general, this means minimizing the amount of data transfer across nodes, since this is usually the bottleneck for big data analysis problems. Spark tuning and optimization is complicated - this tutorial only touches on some of the basic concepts.
The Spark Programming Guide. Spark functions such as map can use variables defined in the driver program, but they make local copies of the variable that are not passed back to the driver program.
Accumulators are shared variable that allow the aggregation of results from workers back to the driver program, for example, as an event counter. Suppose we want to count the number of rows of data with missing information.
The most efficient way is to use an accumulator. Sometimes we need to send a large read only variable to all workers. For example, we might want to share a large feature matrix to all workers as a part of a machine learning application. This same variable will be sent separately for each parallel operation unless you use a broadcast variable.
Also, the default variable passing mechanism is optimized for small variables and can be slow when the variable is large. The worker checks if the path has been cached and uses the cache instead of loading from the path.
Some events trigger the redistribution of data across partitions, and involves the expensive copying of data across executors and machines. This is known as the shuffle. For example, if we do a reduceByKey operation on key-value pair RDD, Spark needs to collect all pairs with the same key in the same partition to do the reduction. In particular, you can ask Spark to partition a set of keys so that they are guaranteed to appear together on some node.
This can minimize a lot of data transfer. Every night, you want to update with new user comments with a join operation. There is a lot of unnecessary data transfer. This is done by rdd.
Suppose it is more convenient or efficient to write a function in some other language to process data. We can pipe data from Spark to the external program script that performs the calculation via standard input and output. Shared variables Accumulators Broadcast variables DataFrames Partitioning and the Spark shuffle Piping to external programs Spark tuning and optimization is complicated - this tutorial only touches on some of the basic concepts. In :. In :.
Understanding Partitioning in Spark | Partitioning Techniques
In :. In :. You may copy it, give it away or', 're-use it under the terms of the Project Gutenberg License included', 'with this eBook or online at www. We want to count the number of non-empty lines. In :.
In :. In :. In :. In :. In :. In :. In :. In :. In :.Spark writers allow for data to be partitioned on disk with partitionBy.
Some queries can run 50 to times faster on a partitioned data lake, so partitioning is vital for certain queries. This blog post discusses how to use partitionBy and explains the challenges of partitioning production-sized datasets on disk.
Understanding Partitioning in Spark | Partitioning Techniques
Different memory partitioning tactics will be discussed that let partitionBy operate more efficiently. By default, Spark does not write data to disk in nested folders.
Memory partitioning is often important independent of disk partitioning. Creating one file per disk partition is not going to work for production sized datasets.
The partitionBy writer will write out files on disk for each memory partition. The maximum number of files written out is the number of unique countries multiplied by the number of memory partitions.
We only have 5 rows of data, so only 5 files are written in this example. This technique helps us set a maximum number of files per partition when creating a partitioned lake. Each disk partition will have up to 8 files. The data is split randomly in the 8 memory partitions. This is better, but still not ideal. We have 4 files for Cuba and seven files for France, so too many small files are being created. This technique is particularity important for partition keys that are highly skewed.
The number of inhabitants by country is a good example of a partition key with high skew. For example Jamaica has 3 million people and China has 1. The maxRecordsPerFile option was added in Spark 2. Use the tactics outlined in this blog post to build your partitioned data lakes and start them off without the small file problem! Partitioned data lakes can be much faster to query when filtering on the partition keys because they allow for a massive data skipping.
Creating and maintaining partitioned data lakes is challenging, but the performance gains make them a worthwhile effort. Great article, really very helpful. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Skip to content. Creating and maintaining partitioned data lake is hard. Memory partitioning vs. File ". We can use the maxRecordsPerFile option to output files with 10 rows.
Partitioning dataset with max rows per file pre Spark 2.Partitioning is nothing but dividing it into parts. If you talk about partitioning in distributed system, we can define it as the division of the large dataset and store them as multiple parts across the cluster. Spark works on data locality principle.
Worker nodes takes the data for processing that are nearer to them. To divide the data into partitions first we need to store it. Spark stores its data in form of RDDs. Each RDD is split into multiple partitions which may be computed on different nodes of the cluster. In Spark, every function is performed on RDDs only. Spark revolves around the concept of a resilient distributed dataset RDDwhich is a fault-tolerant collection of elements that can be operated on in parallel.
In Hadoop, we store the data as blocks and store them in different data nodes. In Spark, instead of following the above approach, we make partitions of the RDDs and store in worker nodes datanodes which are computed in parallel across all the nodes. In Hadoop, we need to replicate the data for fault recovery, but in case of Spark, replication is not required as this is performed by RDDs.
RDDs load the data for us and are resilient which means they can be recomputed. RDDs perform two types of operations: Transformations which creates a new dataset from the previous RDD and actions which return a value to the driver program after performing the computation on the dataset.
RDDs keeps a track of transformations and checks them periodically. If a node fails, it can rebuild the lost RDD partition on the other nodes, in parallel. Spark has two types of partitioning techniques. One is HashPartitioner and the other is RangePartitioner.
Let us see about each of them in detail. The concept of hashcode is that objects which are equal should have the same hashcode. So based on this hashcode concept HashPartitioner will divide the keys that have the same hashcode.Window also, windowing or windowed functions perform a calculation over a set of rows.
It is an important tool to do statistics. Most Databases support Window functions. Spark from version 1. Please refer to spark-window-function on medium. There are hundreds of general spark functions in which Aggregate Functions and Window Functions categories are related to this case.
Spark will throw out an exception when running it. The values are only from unboundedPreceding until currentRow. The following example keeps the top 2 employees salary wise, others have to go. On the sample dataset, Wilma and Maja have the same salary. Maja has to go according to order, unfortunately. The real values we get are depending on the order.
Notice from the output, the first row in a window with lag will have value nulland the last row in a window with lead will have value null. We can calculate the difference with lead and lag compare the currentRow. We can replace the value null after getting the difference. Or like this example, using when to calculate the difference, fill in a literal value, e.
We can, after calculating the difference, find some outliers which have a huge salary gap. The following example takes employees whose salary is double to the next employee. Running Total means adding everything up until the currentRow. The parameters value can be Window. Or a value relative to Window. The difference compares to rowsBetween is that it compare with value of the current row.
Here is an example use directly after Window.
Spark Window Function - PySpark
We can see from the output that the data in the window is random. Here is an example use after Window. The data in the window is ordered. In certain cases median are more robust comparing to mean, since it will filter out outlier values. We can either using Window function directly or first calculate the median value, then join back with the original data frame.
We can calculate the median value first, then join back with the original DataFrame. Here is an example. Spark Window Functions have the following traits: perform a calculation over a group of rows, called the Frame.
Just import them all here for simplicity.