Data partitioning is critical to data processing performance especially for large volume of data processing in Spark. When processing, Spark assigns one task for each partition and each worker threads can only process one task at a time. Python is used as programming language in the examples. You can choose Scala or R if you are more familiar with them.
The above scripts instantiates a SparkSession locally with 8 worker threads. For the above code, it will prints out number 8 as there are 8 worker threads. By default, each thread will read data into one partition. There are two functions you can use in Spark to repartition data and coalesce is one of them.
Spark Read and Write JSON file into DataFrame
Returns a new :class: DataFrame that has exactly numPartitions partitions. Similar to coalesce defined on an :class: RDDthis operation results in a narrow dependency, e.
If a larger number of partitions is requested, it will stay at the current number of partitions. The answer is still 8. In the above code, we want to increate the partitions to 16 but the number of partitions stays at the current 8.
If we decrease the partitions to 4 by running the following code, how many files will be generated? The other method for repartitioning is repartition. Returns a new :class: DataFrame partitioned by the given partitioning expressions. The resulting DataFrame is hash partitioned.
If it is a Column, it will be used as the first partitioning column. If not specified, the default number of partitions is used.
Added optional arguments to specify the partitioning columns. Also made numPartitions optional if partitioning columns are specified. Spark will try to evenly distribute the data to each partitions. If the total partition number is greater than the actual record count or RDD sizesome partitions will be empty. After we run the above code, data will be reshuffled to 10 partitions with 10 sharded files generated.
The above scripts will create partitions Spark by default create partitions. However only three sharded files are generated:. If you look into the data, you may find the data is probably not partitioned properly as you would expect, for example, one partition file only includes data for both countries and different dates too.
This is because by default Spark use hash partitioning as partition function. You can use range partitioning function or customize the partition functions. I will talk more about this in my other posts. In real world, you would probably partition your data by multiple columns.
For example, we can implement a partition strategy like the following:.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. Skip to content. Tags: apache parquetapache parquet sparkspark read parquetspark write parquet. Pickard 14 Nov Reply. Great tutorial, thank you! Leave a Reply Cancel reply. Close Menu.Spark SQL provides spark. Using spark. It also reads all columns as a string StringType by default.
I will explain in later sections on how to inferschema the schema of the CSV which reads the column names from header and column type from data. Using the spark. We can read all CSV files from a directory into DataFrame just by passing directory as a path to the csv method. By default, it is commacharacter, but can be set to any character us this option. It requires to read the data one more time to infer the schema. This option is used to read the first line of the CSV file as column names.
Supports all java. SimpleDateFormat formats. Note: Besides the above options, Spark CSV dataset also supports many other options, please refer to this article for details. If you know the schema of the file ahead and do not want to use the inferSchema option for column names and types, use user-defined custom column names and type using schema option.
Please refer to the link for more details. While writing a CSV file you can use several options. Spark DataFrameWriter also has a method mode to specify SaveMode; the argument to this method either takes below string or a constant from SaveMode class. In this tutorial, you have learned how to read a CSV file, multiple csv files and all files from a local folder into Spark DataFrame, using multiple options to change the default behavior and write CSV files back to DataFrame using different save options.
Kindly help. Thanks in Advance. Actually headers in my csv file starts from 3rd row? How can I configure in such cases?
Your help is highly appreciated. Huge fan of the website. I was trying to read multiple csv files located in different folders as:. However, when running the program from spark-submit says that spark module not found. Thanks Divyesh for your comments. Could you please share your complete stack trace error? If you have already resolved the issue, please comment here, others would get benefit from your solution.
Skip to content. Ashwin s 17 Mar Reply. Hi NNK, We have headers in 3rd row of my csv file. How can I configure such case NNK? Really appreciate your response. NNK 22 Jan Reply. Hi NNK, Could you please explain in code?Make sure this package exists in your Spark environment. Alternatively you can pass in this package as parameter when running Spark job using spark-submit or pyspark command.
For example:. For example, you can change to a different version of Spark XML package. Read XML file. 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.
Error debug You may encounter the following error: py4j. Py4JJavaError: An error occurred while calling o BootstrapMethodError: java. Read XML file Remember to change your file location accordingly. No comments yet. Dark theme mode Dark theme mode is available on Kontext. Kontext Column Created for everyone to publish data, programming and cloud related articles. Follow three steps to create your columns.
Follow us. In this article. Powered by Azure. Features Columns Forums Tags Search. Resources Subscribe RSS. About Cookie Privacy Terms Contact us.The same approach could be used with Java and Python PySpark when time permits I will explain these additional languages. The complete example explained here is available at GitHub project to download. Using spark. When you use format "json" method, you can also specify the Data sources by their fully qualified name i.
Sometimes you may want to read records from JSON file that scattered multiple lines, In order to read such files, use-value true to multiline option, by default multiline option, is set to false. Below is the input file we going to read, this same file is also available at Github. Using the spark. Spark Schema defines the structure of the data, in other words, it is the structure of the DataFrame. If you know the schema of the file ahead and do not want to use the default inferSchema option for column names and types, use user-defined custom column names and type using schema option.
Supports all java. SimpleDateFormat formats. Please refer to the link for more details. Spark DataFrameWriter also has a method mode to specify SaveMode; the argument to this method either takes below string or a constant from SaveMode class. In this tutorial, you have learned how to read a JSON file with single line record and multiline record into Spark DataFrame, and also learned reading single and multiple files at a time and writing JSON file back to DataFrame using different save options.
Skip to content. Tags: jsonmultilineschema. Leave a Reply Cancel reply. Close Menu.In the simplest form, the default data source parquet unless otherwise configured by spark. You can also manually specify the data source that will be used along with any extra options that you would like to pass to the data source. Data sources are specified by their fully qualified name i.
DataFrames loaded from any data source type can be converted into other types using this syntax. The extra options are also used during write operation. For example, you can control bloom filters and dictionary encodings for ORC data sources. For Parquet, there exists parquet.
Save operations can optionally take a SaveModethat specifies how to handle existing data if present. It is important to realize that these save modes do not utilize any locking and are not atomic. Additionally, when performing an Overwritethe data will be deleted before writing out the new data. DataFrames can also be saved as persistent tables into Hive metastore using the saveAsTable command. Notice that an existing Hive deployment is not necessary to use this feature.
Spark will create a default local Hive metastore using Derby for you. Persistent tables will still exist even after your Spark program has restarted, as long as you maintain your connection to the same metastore. A DataFrame for a persistent table can be created by calling the table method on a SparkSession with the name of the table.
For file-based data source, e. When the table is dropped, the custom table path will not be removed and the table data is still there. If no custom table path is specified, Spark will write data to a default table path under the warehouse directory.
When the table is dropped, the default table path will be removed too.
Spark Read Text File | RDD | DataFrame
Starting from Spark 2. This brings several benefits:. Note that partition information is not gathered by default when creating external datasource tables those with a path option. For file-based data source, it is also possible to bucket and sort or partition the output.Python - Read from multiple files & Regex search pattern in files
Bucketing and sorting are applicable only to persistent tables:. Thus, it has limited applicability to columns with high cardinality.
Generic Load/Save Functions
In contrast bucketBy distributes data across a fixed number of buckets and can be used when a number of unique values is unbounded. R" in the Spark repo. When saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown. Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected not to save the contents of the DataFrame and not to change the existing data.Spark provides several ways to read.
Using these methods we can also read all files from a directory and files with a specific pattern. We can read a single text file, multiple files and all files from a directory into Spark RDD by using below two functions that are provided in SparkContext class.
Here, it reads every line in a "text This method also takes the path as an argument and optionally takes a number of partitions as the second argument. When you know the names of the multiple files you would like to read, just input all file names with comma separator and just a folder if you want to read all files from a folder in order to create an RDD and both methods mentioned above supports this.
I will leave it to you to research and come up with an example. Again, I will leave this to you to explore. This complete code is also available at GitHub for reference. Using spark. In case if you want to convert into multiple columns, you can use map transformation and split method to transform, the below example demonstrates this. Yields below output. This splits all elements in a Dataset by delimiter and converts into a Dataset[Tuple2].
Also, you learned how to read multiple text files, by pattern matching and finally reading all files from a folder. Skip to content. Tags: read text file into DataFrameread text file into datasetread text file into rddspark read text filetextFilewholeTextFiles. Leave a Reply Cancel reply. Close Menu.