Pyspark Read Parquet With Schema


DataFrameWriter. Input Sources. Apache Spark. find the most popular…. In the example xml dataset above, I will choose "items" as my classifier and create the classifier as easily as follows:. # read the model and parse through each column # if the row in model is present in df_columns then replace the default values # if it is not present means a new column needs to be added,. 11 to use and retain the type information from the table definition. Apache Kudu is a recent addition to Cloudera's CDH distribution, open sourced and fully supported by Cloudera with an enterprise subscription. Use Spark SQL for ETL. Spark SQL 10 Things You Need to Know 2. Diving into Spark and Parquet Workloads, by Example Topic: In this post you can find a few simple examples illustrating important features of Spark when reading partitioned tables stored in Parquet, in particular with a focus on performance investigations. parquet function that returns an RDD of JSON strings using the column names and schema to. Apache Parquet saves data in column oriented fashion, so if you need 3 columns, only data of those 3 columns get loaded. df reads in a dataset from a data source as a DataFrame. Internally, Spark SQL uses this extra information to perform extra optimization. It means you need to read each field by splitting the whole string with space as a delimiter and take each field type is String type, by default. SPARK-11868 wrong results returned from dataframe create from Rows without consistent schma on pyspark Resolved SPARK-13740 add null check for _verify_type in types. This also facilitates use with dynamic, scripting languages, since data, together with its schema, is fully self-describing. A Databricks database is a collection of tables. Contribute to lightcopy/parquet-index development by creating an account on GitHub. Spark SQL, DataFrames and Datasets Guide. Spark Packages is a community. This approach is useful if you have a seperate parquet file per day, or if there is a prior step in your pipeline that outputs hundreds of parquet files. csv or Panda's read_csv, with automatic type inference and null value handling. This YouTube data is publicly available and the data set is described below under the heading Dataset Description. Dataframes can be saved into HDFS as Parquet files. Lets begin the tutorial and discuss about the SparkSQL and DataFrames Operations using Spark 1. We are trying to use “aliases” on field names and are running into issues while trying to use alias-name in SELECT. The following example demonstrates how to read a Parquet file in a MapReduce job; portions of code specific to Parquet are shown. StructField(). Developers. When reading CSV files with a user-specified schema, it is possible that the actual data in the files does not match the specified schema. apache spark sql and dataframe guide. We then query and analyse the output with Spark. Above code will create parquet files in input-parquet directory. Supported file formats are text, csv, json, orc, parquet. csv文件,里面有四列数据,长 博文 来自: 幸运的Alina的博客 【. sql import SQLContext sqlContext = SQLContext(sc) sqlContext. x line and has a lot of new improvements. Hive与Parquet在处理表schema信息的区别: a)Hive不区分大小写,Parquet区分大小写; b)Hive需要考虑列是否为空,Parquet不需要考虑;. As it turns out, real-time data streaming is one of Spark's greatest strengths. mergeSchema``. Apache Spark is written in Scala programming language. parquet("") this code snippet will be executed by python, and the python will call spark driver, the spark driver will launch tasks in spark executors, so your Python is just a client to invoke job in Spark Driver. Article Introduction. You can check the size of the directory and compare it with size of CSV compressed file. What gives? Works with master='local', but fails with my cluster is specified. As with all Spark integrations in DSS, PySPark recipes can read and write datasets, whatever their storage backends. Exploring querying parquet with Hive, Impala, and Spark November 20, 2015 At Automattic , we have a lot of data from WordPress. Background Apache Spark is a general-purpose cluster computing engine with APIs in Scala, Java and Python and libraries for streaming, graph processing and machine learning. Reading and Writing the Apache Parquet Format¶. In this post I will try to explain what happens when Apache Spark tries to read a parquet file. Power at scale High performance on petabyte-scale data volumes With its unique cost-based query optimizer designed for large-scale data workloads, Greenplum scales interactive and batch-mode analytics to large datasets in the petabytes without degrading query performance and throughput. pyspark SparkDataSet (filepath, file_format='parquet You can find a list of read options for each supported format in Spark DataFrame read. Using the Example helper classes in the Parquet JAR files, a simple map-only MapReduce job that reads Parquet files can use the ExampleInputFormat class and the Group value class. sql into multiple files. If you want to read data from a DataBase, such as Redshift, it's a best practice to first unload the data to S3 before processing it with Spark. A SQLContext can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Parquet tables created by Impala can be accessed by Hive, and vice versa. from pyspark import SparkContext Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original. Main entry point for Spark SQL functionality. It was created originally for use in Apache Hadoop with systems like Apache Drill, Apache Hive, Apache Impala (incubating), and Apache Spark adopting it as a shared standard for high performance data IO. It is now, essentially, a nested table. Please rescue. In this Spark Tutorial – Read Text file to RDD, we have learnt to read data from a text file to an RDD using SparkContext. With a SQLContext, we are ready to create a DataFrame from our existing RDD. SPARK-11868 wrong results returned from dataframe create from Rows without consistent schma on pyspark Resolved SPARK-13740 add null check for _verify_type in types. We are going to load this data, which is in a CSV format, into a DataFrame and then we. 4 读取csv文件 2. You can set the following Parquet-specific option(s) for reading Parquet files: * ``mergeSchema``: sets whether we should merge schemas collected from all \ Parquet part-files. プロパティ名 デフォルト 意味; spark. These systems allow you to query Parquet files as tables using SQL-like syntax. Tutorial: Access Data Lake Storage Gen2 data with Azure Databricks using Spark. from pyspark. from pyspark import SparkContext Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original. csv having below data and I want to find a list of customers whose salary is greater than 3000. Reading Parquet Files in MapReduce. Compared to any traditional approach where the data is stored in a row-oriented format, Parquet is more efficient in the terms of performance and storage. Note that the main difference is that I am attempting to rewrite the file. Spark SQL module also enables you to access a variety of data sources, including Hive, Avro, Parquet, ORC, JSON, and JDBC. How does Flexter generate the target schema? We generate the target schema based on the information from the XML, the XSD, or a combination of the two. They are extracted from open source Python projects. parquet with different schema (There are multiple levels which I dont want to replicate all over the HDFS for different objects with same path) and since spark enforces lazy evaluation, wont be reading will be taken care of by proper filters. Python For Data Science Cheat Sheet PySpark - SQL Basics Learn Python for data science Interactively at www. If you're going to specify a custom schema you must make sure that schema matches the data you are reading. When the input format is supported by the DataFrame API e. Something we've only begun to touch on so far is the benefit of utilizing Apache Spark is larger-scale data pipelines. "How can I import a. The reconciliation rules are: Fields that have the same name in both schema must have the same data type regardless of nullability. Plenty of handy and high-performance packages for numerical and statistical calculations make Python popular among data scientists and data engineer. Here we have taken the FIFA World Cup Players Dataset. I am trying to get rid of white spaces from column names - because otherwise the DF cannot be saved as parquet file - and did not find any usefull method for renaming. PySpark SQL Cheat Sheet. Parquet is a self-describing columnar file format. You can set the following Parquet-specific option(s) for reading Parquet files: * ``mergeSchema``: sets whether we should merge schemas collected from all \ Parquet part-files. This means that you can cache, filter, and perform any operations supported by DataFrames on tables. SQLOne use of Spark SQL is to execute SQL queries. In this post I will try to explain what happens when Apache Spark tries to read a parquet file. Apache Spark. Just pass the columns you want to partition on, just like you would for Parquet. In the couple of months since, Spark has already gone from version 1. parquet the schema inference inside PySpark (and maybe Scala Spark as well) only looks at. StructType(). This post is about analyzing the Youtube dataset using pyspark dataframes. Step 6: Output. If your Parquet or Orc files are stored in a hierarchical structure, the AWS Glue job fails with the "Unable to infer schema" exception. The interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. When reading CSV files with a user-specified schema, it is possible that the actual data in the files does not match the specified schema. class pyspark. 248 249 The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can 250 utilize the relational query api exposed by SparkSQL. I am trying to get rid of white spaces from column names - because otherwise the DF cannot be saved as parquet file - and did not find any usefull method for renaming. Spark SQL module also enables you to access a variety of data sources, including Hive, Avro, Parquet, ORC, JSON, and JDBC. # read in the parquet file created above # parquet files are self-describing so the schema is preserved # the result of. How do I read a parquet in PySpark written from Spark? 0 votes. parquet ("people. # NOTE: For REPL sessions, your humble author prefers ptpython with vim(1) key bindings. IllegalArgumentException: problem reading. class petastorm. They are extracted from open source Python projects. In this recipe we'll learn how to save a table in Parquet format and then how to load it back. I want to read a parquet file with Pyspark. After installing the xsd2er package, go to command prompt and enter xsd2er. sql('select * from massive_table') df3 = df_large. class petastorm. The second option to create a dataframe is to read it in as RDD and change it to dataframe by using the toDF dataframe function or createDataFrame from SparkSession. In this recipe we’ll learn how to save a table in Parquet format and then how to load it back. the input is JSON (built-in) or Avro (which isn't built in Spark yet, but you can use a library to read it) converting to Parquet is just a matter of reading the input format on one side and persisting it as Parquet on the other. Spark SQL provides an option for querying JSON data along with auto-capturing of JSON schemas for both. Trying to write auto partitioned Dataframe data on an attribute to external store in append mode overwrites the parquet files. 3, released on February 2018, is the fourth release in 2. I am converting JSON to parquet file conversion using df. 1> RDD Creation a) From existing collection using parallelize meth. Reading with Hive a Parquet dataset written by Pig (and vice versa) leads to various issues, most being related to complex types. csv file is in the same directory as where pyspark was launched. 247 """An RDD of L{Row} objects that has an associated schema. How do I read a parquet in PySpark written from Spark? Ask Question Asked 2 years, 4 months How to specify schema while reading parquet file with pyspark? 0. Typically these files are stored on HDFS. Spark SQL can also be used to read data from an existing Hive installation. Spark SQL - 10 Things You Need to Know 1. schema from avro. In this lab we will learn the Spark distributed computing framework. What would be the best approach to handle this use case in PySpark?. For example, you can read and write Parquet files using Pig and MapReduce jobs. SPARK-11868 wrong results returned from dataframe create from Rows without consistent schma on pyspark Resolved SPARK-13740 add null check for _verify_type in types. Tutorial: Access Data Lake Storage Gen2 data with Azure Databricks using Spark. To work with Hive, we have to instantiate SparkSession with Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions if we are using Spark 2. To provide you with a hands-on-experience, I also used a real world machine. The problem we're seeing is that if a null occurs in a non-nullable field and is written down to parquet the resulting file gets corrupted and can not be read back correctly. mergeSchema``. The equivalent to a pandas DataFrame in Arrow is a Table. The following example illustrates how to read a text file from Amazon S3 into an RDD, convert the RDD to a DataFrame, and then use the Data Source API to write the DataFrame into a Parquet file on Amazon S3: Specify Amazon S3 credentials. You can vote up the examples you like or vote down the exmaples you don't like. Spark SQL is a component on top of Spark Core that facilitates processing of structured and semi-structured data and the integration of several data formats as source (Hive, Parquet, JSON). Generate Schema. The reason why we are removing this data is because we do not want actual data to take so much space in hdfs location, and for that reason only we have created an PARQUET table. You can set the following Parquet-specific option(s) for reading Parquet files: * ``mergeSchema``: sets whether we should merge schemas collected from all \ Parquet part-files. parquetDF = spark. webpage Output Directory (HDFS): /smartbuy/webpage_files In this exercise you will use Spark SQL to load data from an Impala/Hive table, process it, and store it to a new table. param schema: a :class:`pyspark. The advantages of having a columnar storage are as follows − Spark SQL provides support for both reading and writing parquet files that automatically capture the schema of the original data. Schema inference and explicit definition. So if we are reading data from csv or other sources, we need to explicitly define the schema in our program. It also provides the ability to add new columns and merge schemas that don't conflict. Neil Mukerje is a Solution Architect for Amazon Web Services Abhishek Sinha is a Senior Product Manager on Amazon Athena Amazon Athena is an interactive query service that makes it easy to analyze data directly from Amazon S3 using standard SQL. "header" set to true signifies the first row has column names. Note that the main difference is that I am attempting to rewrite the file. Learning Outcomes. It supports nested data structures. 06/13/2019; 4 minutes to read +3; In this article. UnischemaField [source] ¶ A type used to describe a single field in the schema: name: name of the field. To write data in parquet we need to define a schema. from pyspark. Plenty of handy and high-performance packages for numerical and statistical calculations make Python popular among data scientists and data engineer. How to select particular column in Spark(pyspark)? this is how it can be done using PySpark: Is there any way to read Xlsx file in pyspark?Also want to read. sql import newDF = spark. 251 252 For normal L{pyspark. Spark supports text files (compressed), SequenceFiles, and any other Hadoop InputFormat as well as Parquet Columnar storage. 3, released on February 2018, is the fourth release in 2. The interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. parquet but several others such as dailydata1. In the example xml dataset above, I will choose "items" as my classifier and create the classifier as easily as follows:. 03/11/2019; 7 minutes to read +6; In this article. Learn how to read and save to CSV a Parquet compressed file with a lot of nested tables and Array types. DataFrameWriter. is a columnar file format that provides optimizations to speed up queries and is a far more efficient file format than CSV or JSON. Now this causes an analysis exception like so. If data files are produced with a different physical layout due to added or reordered columns, Spark still decodes the column data correctly. Using Apache Spark on an EMR cluster, I have read in xml data, inferred the schema, and stored it on s3 in parquet format. Article Introduction. parse(open("pair. sql import SparkSession spark = SparkSession. I am converting JSON to parquet file conversion using df. The documentation for parquet says the format is self describing, and the full schema was available when the parquet file was saved. A SparkSession can be used to create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Simply running sqlContext. Background Apache Spark is a general-purpose cluster computing engine with APIs in Scala, Java and Python and libraries for streaming, graph processing and machine learning. The following example demonstrates how to read a Parquet file in a MapReduce job; portions of code specific to Parquet are shown. The matter is that i am able to save it but if i want to read it back i am getting these errors: - Could not read footer for file file´status - unable to specify Schema Any Suggestions?. The Parquet files created by this sample application could easily be queried using Shark for example. In Azure data warehouse, there is a similar structure named "Replicate". dataframe创建 2. """Loads a Parquet file stream, returning the result as a :class:`DataFrame`. py # Use python(1) if you don’t use ptpython. Tutorial: Access Data Lake Storage Gen2 data with Azure Databricks using Spark. Hi, I was working on a project to convert snowplow shredded JSON to Parquet to be able to run some analysis on AWS Athena. Note that the main difference is that I am attempting to rewrite the file. Automatic schema conversion Supports most conversions between Spark SQL and Avro records, making Avro a first-class citizen in Spark. parquet the schema inference inside PySpark (and maybe Scala Spark as well) only looks at. In the middle of the code, we are following Spark requirements to bind DataFrame to a temporary view. Prepare your clickstream or process log data for analytics by cleaning, normalizing, and enriching your data sets using AWS Glue. parquet(" people. "inferSchema" instructs Spark to attempt to infer the schema of the CSV and finally load function passes in the path and name of the CSV source file. Avro is used as the schema format. It is now, essentially, a nested table. write_schema (columns) ¶ Write the dataset schema into the dataset JSON definition file. Exploring querying parquet with Hive, Impala, and Spark November 20, 2015 At Automattic , we have a lot of data from WordPress. In particular, Parquet is shown to boost Spark SQL performance by 10x on average compared to using text. 2Writing temporary data to HDFS You can materialize a pyspark. Parquet tables created by Impala can be accessed by Hive, and vice versa. Hi All, unfortunately I have an hard problem with Spark and Scala programming. SparkSession(sparkContext, jsparkSession=None)¶. Read a text file in Amazon S3:. Message view « Date » · « Thread » Top « Date » · « Thread » From: SkyFox Subject: java. That's why Whenever possible, use functions from. Search results for parquet. "header" set to true signifies the first row has column names. The interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. What gives? Works with master='local', but fails with my cluster is specified. MEMORY_ONLY_SER): """Sets the storage level to persist its values across operations after the first time it is computed. Please rescue. [2/4] spark git commit: [SPARK-5469] restructure pyspark. is a columnar file format that provides optimizations to speed up queries and is a far more efficient file format than CSV or JSON. Spark SQL can read and write Parquet files. Pyspark: Parse a column of json strings How to handle changing parquet schema in Apache Spark Reading CSV into a Spark Dataframe with timestamp and date types. DataFrame with a schema below:. The following are code examples for showing how to use pyspark. This means that the saved file will take up less space in HDFS and it will load faster if you read the data again later. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). from pyspark import SparkContext Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original. One of the notable improvements is ORC suppor…. `Now that we're comfortable with Spark DataFrames, we're going to implement this newfound knowledge to help us implement a streaming data pipeline in PySpark. We have set the session to gzip compression of parquet. Row object while ensuring schema HelloWorldSchema compliance (shape, type and is-nullable condition are tested). Spark SQL provides support for both reading and writing parquet files that automatically capture the schema of the original data, so there is really no reason not to use Parquet when employing Spark SQL. One way that this can occur is if a long value in python overflows the sql LongType, this results in a null value inside the dataframe. I have narrowed the failing dataset to the first 32 partitions of the data:. Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. write_with_schema (dataset, dataframe, delete_first=True) ¶ Writes a SparkSQL dataframe into an existing DSS dataset. pyspark-Spark SQL, DataFrames and Datasets Guide. In my last post on this topic, we loaded the Airline On-Time Performance data set collected by the United States Department of Transportation into a Parquet file to greatly improve the speed at which the data can be analyzed. engine, interfaces Python commands with a Java/Scala execution core, and thereby gives Python programmers access to the Parquet format. Internally, Spark SQL uses this extra information to perform extra optimization. An instance of Unischema is serialized as a custom field into a Parquet store metadata, hence a path to a dataset is sufficient for reading it. File source - Reads files written in a directory as a stream of data. Data Engineers Will Hate You - One Weird Trick to Fix Your Pyspark Schemas May 22 nd , 2016 9:39 pm I will share with you a snippet that took out a lot of misery from my dealing with pyspark dataframes. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. When Avro data is read, the schema used when writing it is always present. Currently, Spark looks up column data from Parquet files by using the names stored within the data files. PySpark()(Data(Processing(in(Python(on(top(of(Apache(Spark Peter%Hoffmann Twi$er:(@peterhoffmann github. class pyspark. param schema: a :class:`pyspark. Simply running sqlContext. Message view « Date » · « Thread » Top « Date » · « Thread » From: SkyFox Subject: java. Dataframe Creation. It can also take in data from HDFS or the local file system. You can set the following Parquet-specific option(s) for reading Parquet files: * ``mergeSchema``: sets whether we should merge schemas collected from all \ Parquet part-files. Sample schema, where each field has both a name and a alias:. from pyspark. They are extracted from open source Python projects. DataFrame with a schema below:. sql import newDF = spark. I wrote the following codes. Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a Hive metastore Parquet table to a Spark SQL Parquet table. As it turns out, real-time data streaming is one of Spark's greatest strengths. The matter is that i am able to save it but if i want to read it back i am getting these errors: - Could not read footer for file file´status - unable to specify Schema Any Suggestions?. df_parquet_w_schema = sqlContext. DataFrame(). I would like to test the my first query in Spark, using Scala and. SparkSession(sparkContext, jsparkSession=None)¶. 连接本地spark 2. exploded_fields = [s for s in result. csv一个小坑(转义符居然是") 1. Therefore, roundtrip in reading and writing XML files has the same structure but writing a DataFrame read from other sources is possible to have a different structure. It’s well-known for its speed, ease of use, generality and the ability to run virtually everywhere. This means that the saved file will take up less space in HDFS and it will load faster if you read the data again later. SparkML里的核心API已经换成了DataFrame,为了使读取到的值成为DataFrame类型,我们可以直接使用读取CSV的方式来读取文本文件,可问题来了,当文本文件中每一行的各个数据被不定数目. parquet but several others such as dailydata1. How does Apache Spark read a parquet file. The following example illustrates how to read a text file from Amazon S3 into an RDD, convert the RDD to a DataFrame, and then use the Data Source API to write the DataFrame into a Parquet file on Amazon S3: Specify Amazon S3 credentials. Apache Spark, Parquet, and Troublesome Nulls. Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. PySpark program to convert CSV file(s) to Parquet Must either infer schema from header or define schema (column names) on the command line. You can vote up the examples you like or vote down the exmaples you don't like. appName("Chicago_crime_analysis"). exploded_fields = [s for s in result. Immediately I’m faced with a simple question: Should a file have a Header? I’m guessing, this question is for ML/AI task, and maybe in the future, it will be in my queue to. Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. You can either define the schema programmatically as part of the read operation as demonstrated in this section, or let the platform infer the schema by using the inferSchema option (option("inferSchema", "true")). Exploring querying parquet with Hive, Impala, and Spark November 20, 2015 At Automattic , we have a lot of data from WordPress. How does Flexter generate the target schema? We generate the target schema based on the information from the XML, the XSD, or a combination of the two. In my JSON file all my columns are the string, so while reading into dataframe I am using schema to infer and the reason for that no of. In this blog post, we introduce Spark SQL's JSON support, a feature we have been working on at Databricks to make it dramatically easier to query and create JSON data in Spark. parquet function that returns an RDD of JSON strings using the column names and schema to. Another benefit is that since all data in a given column is the same datatype (obviously), compression quality is far superior. Dataframe Creation. Aside from that, partitions can also be fairly costly if the amount of data is small in each partition. If you are doing this on the master node of the ODROID cluster, that is far too large for the eMMC drive. insertInto(tableName, overwrite=False)[source] Inserts the content of the DataFrame to the specified table. I have narrowed the failing dataset to the first 32 partitions of the data:. Reading and Writing the Apache Parquet Format¶. Typically these files are stored on HDFS. Parquet stores nested data structures in a flat columnar format. Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Avro is used as the schema format. def parquet (self, path): """Loads a Parquet file stream, returning the result as a :class:`DataFrame`. I would like to test the my first query in Spark, using Scala and. Spark SQL 10 Things You Need to Know 2. PySpark Dataframe Sources. according either an avro or parquet schema. parquet(" people. As per the SPARK API latest documentation def text(path: String): Unit Saves the content of the [code ]DataFrame[/code] in a text file at the specified path. Both consist of a set of named columns of equal length. class petastorm. How does Apache Spark read a parquet file. class pyspark. Search results for parquet. This can be used to indicate the type of columns if we cannot infer it automatically. “inferSchema” instructs Spark to attempt to infer the schema of the CSV and finally load function passes in the path and name of the CSV source file. StructField(). The documentation for parquet says the format is self describing, and the full schema was available when the parquet file was saved. The following are code examples for showing how to use pyspark. DataFrameWriter. Parquet is a famous file format used with several tools such as Spark. This will override ``spark. The example above works conveniently if you can easily load your data as a dataframe using PySpark’s built-in functions. 创建dataframe 2. 2) The problem here rises when you have parquet files with different schema and force the schema during read. For demo purposes I simply use protobuf. dataframe创建 2. Sometimes, the schema of a dataset being written is known only by the code of the Python script itself. Once we have a pyspark. schema (pyarrow. Data Engineers Will Hate You - One Weird Trick to Fix Your Pyspark Schemas May 22 nd , 2016 9:39 pm I will share with you a snippet that took out a lot of misery from my dealing with pyspark dataframes. What gives? Works with master='local', but fails with my cluster is specified. class pyspark. 1 (one) first highlighted chunk. You can set the following Parquet-specific option(s) for reading Parquet files: * ``mergeSchema``: sets whether we should merge schemas collected from all \ Parquet part-files. The second option to create a dataframe is to read it in as RDD and change it to dataframe by using the toDF dataframe function or createDataFrame from SparkSession. One cool feature of parquet is that is supports schema evolution. Compression. df_parquet_w_schema = sqlContext. Inferring the schema works for ad hoc analysis against smaller datasets. This will override ``spark. You can find the lineage output of the above example below:. sql import SQLContext sqlContext = SQLContext(sc) sqlContext.