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Spark read parquet with schema

spark read parquet with schema Extract the spark read parquet schema is to update their own struct schema is converted as operations against the core challenge when a valid spark? Allowed to both the spark read parquet schema Set the Apache Spark property spark. schema(schema) . option("inferSchema", "true"). sql import functions as f from pyspark. In this article, I am going to demo how to use Spark to support schema merging scenarios such as adding or deleting columns. option("inferSchema", "true")\ . close # Read data from an avro file with open ('users. Read Write Parquet Files using Spark Problem: Using spark read and write Parquet Files , data schema available as Avro. key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a parquetDF = spark. I want to make a job that reads a parquet file, in order to apply transformations to the data. equals(PARQUET)) { SQLContext sqlContext = new SQLContext (sc); DataFrame parquetFile = sqlContext. ORC also stores schema information with a file so reading ORC data is as easy as reading parquet in Spark. parquet. printSchema () # Count all dataframe . option("header", "true") . Basically the library allows you to bulk load parquet files in one spark command: > spark > . parquet(*(subdirs[:i] + subdirs[i+1:32])) Another strange thing is that the schemas for the first and the last 31 partitions of the subset are identical: In [84]: spark. Spark SQL can read and write Parquet files. read . show() >>> df2 = spark. read. 5x less data for Parquet than Avro. read. This will override spark. append ({'name': 'Pierre-Simon Laplace', 'age': 77}) writer. read. s3a. Usage: $ hadoop jar parquet-tools-1. DataFrame. col_name'. option("sep", "\t")\ . This is determined by the property spark. io spark. write. s3a. RDD (Resilient Distributed Dataset) is the basic abstraction in Spark. ORC vs PARQUET. write. parquet. 1-SNAPSHOT. csv ("src/main/resources/zipcodes. sql. Parquet Compatibility • Native support for reading data in Parquet – Columnar storage avoids reading unneeded data – RDDs can be written to parquet files, preserving the schema 46 // SchemaRDD can be stored as Parquet people. read. Follow below link: http://maven. concatInputPath(inputPath)); textFile. I was able to attach metadata to columns and read that metadata from spark and by using ParquetFileReader It would be nice if we had a way to manipulate parquet metadata directly from DataFrames though. Trigger import org. I will explain in later sections on how to read the schema (inferschema) from the header record and derive the column type based on the data. access. # Create a dataframe object from a parquet file dataframe = spark . toDF. show(truncate = false) val tsAndStringSchema = new StructType (). apache. mergeSchema): sets whether we should merge schemas collected from all Parquet part-files. schema. sql. To read CSV data using a Spark DataFrame, Spark needs to be aware of the schema of the data. sql. It corrects the schema, starts the timer and submits the insertion job to spark. Caused by: java. 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: Overall, Parquet showed either similar or better results on every test. parquet") # Read in the Parquet file created above. You will find the complete list of parameters on the official spark website. fs. hadoop. >>> from pyspark. fields == spark. sql. To read Parquet files in Spark SQL, use the SQLContext. apache. types. read . [2] PARQUET_SCHEMA_MERGING_ENABLED: When true, the Parquet data source merges schemas collected from all data files, otherwise the schema is picked from the summary file or a random data file if Is there a dask equivalent of spark's ability to specify a schema when reading in a parquet file? Possibly using kwargs passed to pyarrow? I have a bunch of parquet files in a bucket but some of the fields have slightly inconsistent names. secret. registerTempTable ("parquetFile") Parquet is a columnar format that is supported by many other data processing systems, Spark SQL support for both reading and writing Parquet files that automatically preserves the schema of the original data. # Create a dataframe object from a parquet file dataframe = spark . compression = 'gzip') >>> pd. parquet. count // Count Next SPARK SQL In this post we will discuss about the loading different format of data to the pyspark. parquet(list(input_dirs)[0]) if schema is not None: # TODO: This only allows extra top level columns, anything # nested must be exactly the same. Now let's demonstrate how to use Spark SQL in java using spark. write. 6. hadoop. #Read the orc file format read_orc = spark. split(",")) >>> people = parts. orc') read_orc. secret. json, spark. sql. fieldNames or df. parquet ("people. StructType for the input schema or a DDL-formatted string; path : string, or list of strings, for input path(s), or RDD of Strings storing CSV rows. _jreader = self. parquet(parquetPath) val df = spark. ignoreCorruptFiles to true and then read the files with the desired schema. json(IN_DIR + '*. splitSize). Parquet's summary files, database table), Spark SQL tries to read this information and contsruct correct schema. // The result of loading a Parquet file is also a SchemaRDD. In R, with the read. option ("delimiter", "t"). parquet") where /tmp/dataset. schema(schema). sql. Though Spark is more optimized to work with parquet file format, it also understands ORC file format well. df = spark. contains ("Spark")). printSchema () # Count all dataframe . If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. applySchema(nestedRDD, nested. unstructured data: log lines, images, binary files. schema) The above code throws an org. Moreover, Parquet features minimum and maximum value statistics at different levels of granularity. join(input_files))) df = spark. toDF flatDF. By default, Parquet will access columns by name and ORC by index (ordinal value). The query-performance differences on the larger datasets in Parquet’s favor are partly due to the compression results; when querying the wide dataset, Spark had to read 3. coalesce(this. Its advanced architecture enables high reliability and low latency through the use of techniques such as schema Also doublecheck that you used any recommended compatibility settings in the other tool, such as spark. No need to use Avro, Protobuf, Thrift or other data serialisation systems. Converting to Avro helps validate the data types and also facilitates efficient conversion to Parquet as the schema is already defined. With schema evolution, one set of data can be stored in multiple files with different but compatible schema. scala > val textFile = spark. // Convert unpartitioned Parquet table at path '<path-to-table>' val deltaTable = DeltaTable. jar With the shell running, you can connect to Parquet with a JDBC URL and use the SQL Context load() function to read a table. getNumPartitions()) outputFileName = "tmp/tsv/2015_summary. spark. Allows you to easily read and write Parquet files in Scala. sql. hadoop. take(10) I expected, actually counted, on this code to throw. json) >>>df. after i added a parition "server" to my partition schema (it was year,month,day and now is year,month,day,server ) and now Spark is having trouwble reading the data. jar --help 5. The typical pipeline to load external data to MySQL is: They all have better compression and encoding with improved read performance at the cost of slower writes. . option("host","yourHost") A Petastorm dataset can be read into a Spark DataFrame using PySpark, where you can use a wide range of Spark tools to analyze and manipulate the dataset. show () Although Spark SQL itself is not case-sensitive, Hive compatible file formats such as Parquet are. show(5,False) Schema of the Parquet File. If none, Spark tries to infer the schema automatically. outputSerialization. /tmp/dog_data_checkpoint/"). These Parquet preserves the schema of the data. Athena is a schema-on-read query engine. conf. Using Spark SQL in Spark Applications. key, spark. sql. to_parquet (path[, mode, …]) Write the DataFrame out as a Parquet file or directory. In this post we’re going to cover the attributes of using these 3 formats (CSV, JSON and Parquet) with Apache Spark. spark读取hive parquet格式的表,是否转换为自己的格式? Apache Parquet is a free and open-source column-oriented data storage format of the Apache Hadoop ecosystem. schema(jschema) elif isinstance(schema, basestring): self. csv(csvPath) val checkpointPath = new java. parquet" ) scala> Parqfile. write. _jreader. github. As we have DataFrameReader, we can specify multiple values. You can use a SparkSession to access Spark functionality: just import the class and create an instance in your code. read. access. Schema) – Use schema obtained elsewhere to validate file schemas. Parquet files are self-describing so the schema is preserved. Parse (json. textFile(this. petastorm_generate_metadata. parquet. dirname(path) for path in input_files) if len(input_dirs) != 1: raise Exception('Expected single directory containing partition data: [{}]'. Now we can use our schema to read the JSON files in our directory. equals(TEXT)) { JavaRDD<String> textFile = sc. parquet”) // Parquet files are self-describing Spark parquet schema evolution. Check if a Field Exists in a DataFrame. Spark SQL must use a case-preserving schema when querying any table backed by files containing Avro is one of the most useful file formats for the data serialization framework in the Spark eco-system because of its language neutrality. sql. saveAsTable("HIVE_DB_NAME. access. sql. read. If your Parquet or Orc files are stored in a hierarchical structure, the AWS Glue job fails with the "Unable to infer schema" exception. sql("select * from nested limit 0") val nestedRDDwithSchema = hc. You can inspect and perform operations on the entered data with the following command sets: Spark SQL allows users to ingest data from these classes of data sources, both in batch and streaming queries. option ("compression", "snappy"). This is because when a Parquet binary file is created, the data type of each column is retained as well. set("spark. add( " display " , StringType ). printSchema() df. read. 8970115788 | | snx001|2020-01-03| [ [1, 3. The count is computed using metadata stored in Parquet file footers. schema("col0 INT, col1 DOUBLE") """ from pyspark. This is the example of the schema on write Unlike CSV and JSON, Parquet files are binary files that contain meta data about their contents, so without needing to read/parse the content of the file (s), Spark can just rely on the header/meta Overwrite). Overwrite). read. _jdf. read . This is a post to index information related to parquet file format and how Spark can use it. qAvro and Parquet - When and Why to use which format? qUse cases for Schema Evolution & practical examples qData modeling - Avro and Parquet schema qWorkshop - Read Avro input from Kafka - Transform data in Spark - Write data frame to Parquet - Read back from Parquet qOur experiences with Avro and Parquet qSome helpful insights for projects Versions: Apache Spark 3. Dependency: Set the Apache Spark property spark. parquet. int :: Nil) spark. parquet ( ). Below are some advantages of storing data in a parquet format. The title of this blog post is maybe one of the first problems you may encounter with PySpark (it was mine). access. getCanonicalPath val parquetPath = new java. createDF ( List ( (1, 2), (3, 4) ), List ( ("num1", IntegerType, true), ("num2", IntegerType, true) ) ) val parquetPath = new java. Below are some advantages of storing data in a parquet format. Logical double value in csv schema pyspark script using sql. set: Different versions of parquet used in different tools (presto, spark, hive) may handle schema changes slightly differently, causing a lot of headaches. 1. textFile(this. This can be accessed through SparkSession through the read attribute shown below: spark. fs. read. parquet") If you have queries related to Spark and Hadoop, kindly refer to our Big Data Hadoop and Spark Community! Inspecting Data. load("parquet-datasets") // The above is equivalent to the following shortcut // Implicitly does format("parquet"). Spark allows the creation of dataframes through multiple sources such as hive, json, parquet, csv and text files that can also be used to create dataframes. Try to read the Parquet dataset with schema merging enabled: spark . expressions. read. This lets Spark quickly infer the schema of a Parquet DataFrame by reading a small file; this is in contrast to JSON where we either need to specify the schema upfront or pay the cost of reading the whole dataset. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). Otherwise, the Parquet filter predicate is not specified. parquet (data_path) df. Spark SQL must use a case-preserving schema when querying any table backed by files containing 1) Parquet schema Vs. option ( "mergeSchema" , "true" ). There are multiple sets of options for different data sources which determines how the data has to be read. schema(schema). parquet. key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a Simple I/O for Parquet. parquet("parquet-events") > > firstRun = false > > } else { // the table has to exist to be able to append data. Let’s talk about Parquet vs Avro Schema on Read . contains("hair:") 4. validate_schema (bool, default True) – Check that individual file schemas are all the same / compatible. parquet("parquet-events read_parquet (path[, columns, index_col, …]) Load a parquet object from the file path, returning a DataFrame. It means you need to read each field by splitting the whole string with space as a delimiter and take each field type is > I would like to import (lots of) Apache parquet files to a PostgreSQL 11. File(". You can also check if two schemas are compatible by using the merge method. In [2]: data_df = spark. read. csv") It also reads all columns as a string (StringType) by default. read. parquet. read. I get the f Re: spark + parquet + schema name and metadata: Date: Thu, 24 Sep 2015 11:25:01 GMT: Hi, your suggestion works nicely. 6. jar schema --help usage: schema [GENERIC-OPTIONS] [COMMAND-OPTIONS] <input> where <input> is the parquet file containing the schema to show. parseDataType(schema. key, spark. Parquet basically only supports the addition of new columns, but what if we have a change like the following : - renaming of a column - changing the type of a column, including… Meant to read schema pyspark script using pyspark shell with. types. scala> import org. orc, spark. parquet"). hadoop. Script to add petastorm metadata to an existing parquet dataset. option("header", "true")\ . Is there a dask equivalent of spark's ability to specify a schema when reading in a parquet file? Possibly using kwargs passed to pyarrow? I have a bunch of parquet files in a bucket but some of the fields have slightly inconsistent names. Parquet File Format. > > jsonRdd. Source files which eventually updates, specify the final result as a second. val parquetFileDF = spark. parquet is your partitioned dataset. One of the places where projects often go off the rails is when multiple datasets are being consolidated. apache. filter (line => line. The main advantage of structured data sources over semi-structured ones is that we know the schema in advance (field names, their types and “nullability”). Since there are already many tutorials to perform various operations in the context, this post mainly consolidate the links. write. Lastly, the schema is optional if data sources provide schema or you intend to provide schema inference. read. 1 and my data is stored in parquet format, the parquet files have been created by Impala. _jreader = self. txt") >>> parts = lines. registerTempTable ( "object" ) scala> val allrecords = sqlContext. read. Download the parquet source code git clone https://github. _jreader. 0 (SPARK-16980) has inadvertently changed the way Parquet logging is redirected and the warnings make their way to the Spark executor's stderr. map(lambda l: l. Therefore, a simple file format is used that provides optimal write performance and does not have the overhead of schema-centric file formats such as Apache Avro and Apache Parquet. 4) Create a sequence from the Avro object which can be converted to Spark SQL Row object and persisted as a parquet file. parquetFile = spark. sql. avro', 'wb') as f: writer = DataFileWriter (f, DatumWriter (), schema_parsed) writer. It then reads the parquet file for the specified table filtered by store_sk. key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a Is there a dask equivalent of spark's ability to specify a schema when reading in a parquet file? Possibly using kwargs passed to pyarrow? I have a bunch of parquet files in a bucket but some of the fields have slightly inconsistent names. pyspark parquet null ,pyspark parquet options ,pyspark parquet overwrite partition ,spark. read. sql. csv("amiradata/pokedex. parquet ( dataset_url ) # Show a schema dataframe . json(“path to the json file”) Assuming the table called ‘nested’ was created as the CREATE TABLE definition earlier, we can use it to infer its schema and apply it to the newly built rdd. orc ('out_orc\part*. mode(SaveMode. Parquet is a columnar format developed by Cloudera and Twitter. csv" flightDataDf = spark. The data producers changed the schema of the table. Spark Read and Write Apache Parquet, Parquet is a columnar file format that provides optimizations to speed up queries and This is an example of how to write a Spark DataFrame into Parquet files Spark SQL provides support for both reading and writing parquet files that automatically capture the schema of the original data. format(“parquet”). In PySpark, parquet() function is available in DataFrameReader and DataFrameWriter to read from and write/create a Parquet file respectively. Hive/Parquet Schema Reconciliation spark. apache. The spark supports the csv as built in source. CombineParquetInputFormat to read small parquet files in one task Problem: Implement CombineParquetFileInputFormat to handle too many small parquet file problem on consumer side. RDD is an immutable distributed collection of elements partitioned across nodes of the cluster that can be operated on in parallel (using low-level API that allow applying transformations and performing actions on the RDD). R spark_read_parquet of sparklyr package. spark. g. Solution: JavaSparkContext => SQLContext => DataFrame => Row => DataFrame => parquet. 默认情况下,Spark会选择其中一个文件来读取Schema。这个做法在多个Parquet文件具有不同Schema时就会有问题。因此,Spark提供了参数spark. foreachRDD( rdd => { > > val dataRdd : RDD[String] = myTransform(rdd) > > val jsonDf = sql. for Parquet written in C (whereas the rest of the DBR is in Scala/Java). avro', 'rb') as f: reader = DataFileReader (f, DatumReader ()) metadata = copy. fs. parquet (path) If you do have Parquet files with incompatible schemas, the snippets above will output an error with the name of the file that has the wrong schema. parquet" ) We can take a look at the schema of the data frames generated and do some preliminary analysis before proceeding further on the data parsed. Here we are doing all these operations in spark interactive shell so we need to use: sc as SparkContext; sqlContext as hiveContext You can generate this non-partitioned Parquet file by using the Spark Shell with the code snippet spark. Build the parquet-tools. write jsonDf to Parquet files: > > if (firstRun) { > > jsonRdd. If set to "true", Spark will use the same convention as Hive for writing the Parquet data. set ("spark. File (". AnalysisException: Union can only be performed on tables with the same number of columns, but the first table has 6 columns and the second table has 7 Parquet file is created from external library; Load the parquet file into Hive/Impala table; Query the table through Impala will fail with below error message incompatible Parquet schema for column 'db_name. Parquet and Spark seem to have been in a love-hate relationship for a while now. `<path-to-table>`") // Convert partitioned Parquet table at path '<path-to-table>' and partitioned by integer columns named 'part' and 'part2' val partitionedDeltaTable = DeltaTable. parquet. Example: Reading and Writing Data Sources From and To Amazon S3. parquet("employee. fields Out[84]: True Figure: Ecosystem of Schema RDD in Spark SQL. load ( "path/to/students. We need to specify the schema of the data we’re going to write in the Parquet file. writeLegacyFormat The default value is false. It obeyed some aspects of my imposed schema (understood that I need to see the column 'foo' it did not have) but ignored its nullable assertion. In turn, it happily spat out: Want to grasp detailed knowledge of Hadoop? Read this extensive Spark Tutorial! From Spark Data Sources JSON >>>df = spark. Then you can use AvroParquetWriter and AvroParquetReader to write and read Parquet files. conf spark. apache. parquet (pathToWriteParquetTo) Then ("We should clean and standardize the output to parquet") val expectedParquet = spark. mergeSchema ): sets whether we should merge schemas collected from all Parquet part-files. It has support for reading csv, json, parquet natively. etl. g. If you have multiple files with different schema , then you need to set one extra option i. createOrReplaceTempView("parquetFile") val Although Spark SQL itself is not case-sensitive, Hive compatible file formats such as Parquet are. We can read the parquet file using two ways. hadoop. io. count(), the Parquet columns are not accessed, instead the requested Parquet schema that is passed down to the VectorizedParquetRecordReader is simply an empty Parquet message. setAppName("Spark Compaction"); JavaSparkContext sc = new JavaSparkContext(sparkConf); if (this. Spark with Parquet file for Fact Table: Now, let’s convert FactInternetSaleNew file to parquet file and save to hdfs using the following command: salesCSV. csv, spark. read. This will help to solve the issue. write. On the one hand, the Spark documentation touts Parquet as one of the best formats for analytics of big data (it is) and on the other hand the support for Parquet in Spark is incomplete and annoying to use. sql. Solution: 1. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. semi-structured data like JSON, CSV or XML. It does not change or rewrite the underlying data. Most of the Spark SQL predicates are supported to use statistics and/or column filter (EqualTo, In, GreaterThan, LessThan, and others). add( " ts " , TimestampType ) Under normal circumstances, failure to parse the metadata does not affect the executor's ability to read the underlying Parquet file but an update to the way Parquet metadata is handled in Apache Spark 2. json()) self. This has a performance impact, depending on the number of rows that need to be scanned to infer the schema. hadoop. petastorm. DataFrame合并schema由哪个配置项控制?2. Parquet is columnar stor-age format, in which data can be compressed using a compression scheme combining dictionary compression, run-length encoding and bit-packing. # Parquet files are self-describing so the schema is preserved. 0. adding or modifying columns. Append). Usage spark_read_parquet(sc, name = NULL, path = name, options = list(), repartition = 0, memory = TRUE, overwrite = TRUE, columns = NULL, schema = NULL, ) Spark read parquet with custom schema, StructType val schema = StructType($"id". read. s3a. 修改配置项的方式有哪两种?3. fs. Multiline JSON files cannot be split, so are processed in a single partition. DataFrame. io. Reading and Writing the Apache Parquet Format¶. show() +----------+----------+--------------------+ |sensorName|sensorDate| sensorReadings| +----------+----------+--------------------+ | snx001|2020-01-10| [ [1, 4. Assigned to be arranged into one of partitioning columns, we create them as a parameter. equals(TEXT)) { JavaRDD<String> textFile = sc. net. rdd. The first will deal with the import and export of any type of data, CSV , text file… Parquet & Spark. format("parquet). Spark SQL must use a case-preserving schema when querying any table backed by files containing Statistics are kept per Parquet block metadata. // copy schema from hive table and apply to RDD val nested = hc. This section provides guidance on handling schema updates for various data formats. parquet (pathToWriteParquetTo) Then ("We should have the correct number of rows") val actualRowCount = expectedParquet. . 9. The SparkSession, introduced in Spark 2. 9999783849 | | snx001|2020-01-01| [ [1, 5. conf. read . mergeSchema (default is the value specified in spark. read. 1-SNAPSHOT</version> 4. read. `<path-to-table>`", "part int, part2 int") Spark SQL provides support for both reading and writing Parquet files that automatically capture the schema of the original data, It also reduces data storage by 75% on average. writeLegacyFormat=true" >>> s = spark. With spark. read. AVRO is ideal in case of ETL operations where we need to query all the columns. read. Val file=sqlContext. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc. e mergeSchema to true , see below code. As a consequence I wrote a short tutorial. [java] Exception in thread “main” org. sql. If you want to check if a Column exists with the same Data Type, then use the Spark schema functions df. , the schema can be modified according to the changes in the data. mode("overwrite")\ . Column type: DECIMAL(19, 0), Parquet schema: optional byte_array col_name [i:2 d:1 r:0] The same query works well in Hive; This is due to impala currently does not support all decimal specs that are supported by Parquet. you might be intersted in spark-postgres library. In [83]: for i in range(32): spark. spark. stream () method. apache. gzip') col1 col2 0 1 3 1 2 4 If you want to get a buffer to the parquet content you can use a io. concatInputPath(inputPath val schema = Seq[MyType](). This is super useful for a framework like Spark, which can use this information to give you a fully formed data-frame with minimal effort. See full list on spark. read. Note that performance also depends on values distribution and predicate selectivity. SQLContext ( sc ) scala> val parqfile = sqlContext. sparkContext df = spark. \ format DataFrameReader val r: DataFrameReader = spark. count () # Show a single and performance (10-100 times faster than Apache Spark on Parquet) to cloud data lakes. Regarding how late a unique identifier column and accordingly return a column, we want to write the queries. Now we are evaluating Parquet format because of its effic AWS Glue’s Parquet writer offers fast write performance and flexibility to handle evolving datasets. write. Even though it's quite mysterious, it makes sense if you take a look at the root cause. key, spark. Reading only a small piece of the Parquet data from a data file or table, Drill can examine and analyze all values for a column across multiple files. conf spark. As you may know, the Parquet format stores the table schema in its footer. key, spark. coalesce(1). parquet (this. This tutorial is based on this article created by Itay Shakury . Below are some advantages of storing data in a parquet format. If it can be loaded, infer Unischema from native Parquet schema. By default, Spark infers the schema from data, however, some times we may need to define our own column names and data types especially while working with unstructured and semi-structured data and this article explains how to define simple, nested and complex schemas with spark. apache. parquetFile. sql import SparkSession from pyspark. See full list on spark. types import * Infer Schema >>> sc = spark. convertToDelta (spark, "parquet. Parquet also reduces StructType val schema = StructType ($ "id". json', schema=sensor_schema) In [3]: data_df. Parquet, and ORC file are columnar file formats. 9464029459 | | snx001|2020-01-07| [ [1, 3. This flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems. write\ . path. Here’s a data schema for a ‘people’ dataset, it’s pretty straight tFileInputParquet - How to read generic parquet files and extract schema? Hello. cgi 2. s3a. schema(schema) Note Some formats can infer schema from datasets (e. Streaming DataFrames can be created through the DataStreamReader interface (Scala / Java / Python docs) returned by SparkSession. Of course, if you change the schema over time, Spark knows how to merge it you must define a special option when reading, but you can only change something in an existing file by Using Avro to define schema. format("csv")\ . Data sources in Apache Spark can be divided into three groups: structured data like Avro files, Parquet files, ORC files, Hive tables, JDBC sources. fs. csv") df. In the shell you can print schema using printSchema method: scala> df. git 3. parquet. Let’s illustrate the differences between these two concepts using some example data and a simple illustrative columnar file format that I just invented. change the datatype of id_sku in your schema to be BinaryType). load("parquet-datasets") Hi, I am using Spark 1. append ({'name': 'John von Neumann', 'age': 53}) writer. col_name'. sql. format("csv")\ . format( '],['. readStream . Like Avro, schema metadata is embedded in the file. When reading a Hive table made of Parquet fil e s, you should notice that Spark has a unique way of relating to the schema of the table. spark. Currently Parquet supports the following specs: Spark SQL supports loading and saving DataFrames from and to a variety of data sources and has native support for Parquet. mergeSchema", "true") spark. parquet. set: spark. val df = spark. The schema is embedded in the data itself, so it is a self-describing data format. /tmp/dog_data_csv/"). Reading JSON, CSV and XML files efficiently in Apache Spark. hadoop. format("parquet"). Input schema. types import StructType,StructField, StringType, IntegerType , BooleanType spark = SparkSession. One cool feature of parquet is that is supports schema evolution. format("parquet") . conf spark. cd parquet-mr/parquet-tools/ mvn clean package -Plocal CSV, TSV, JSON, and Avro, are traditional row-based file formats. filter (line => line. The parquet-compatibility project contains compatibility tests that can be used to verify that implementations in different languages can read and write each other’s files. hadoop. schema(schema). parquet") # Parquet files can also be used to create a temporary view and then used in SQL df. key or any of the methods outlined in the aws-sdk documentation Working with AWS by using the Spark SQL read function such as spark. read . contains ("Spark")) // Transform to a Dataset of lines containing "Spark" scala > textFile. It is supported in Spark, MapReduce, Hive, Pig, Impala, Crunch, and so on. schema(schema). read. parquet") parquetFileDF. schema(schema) else: raise TypeError("schema should be StructType or string") return self if direct_parquet_read: input_files = list(df. types. outputSerialization. read (). mergeSchema (default is the value specified in spark. Reading DataFrame from parquet is simple as: incompatible Parquet schema for column 'db_name. Post category: Apache Spark. import_single_store (url, table_name, store_sk) takes in the url of the database, the table name and the surrogate key (store_sk) of the store. Note: PySpark out of the box supports to read files in CSV, JSON, and many more file formats into PySpark DataFrame. PARQUET is more capable of storing nested data. You may also connect to SQL databases using the JDBC DataSource. The latter is commonly found in hive/Spark usage. parquet("parquet-datasets") // parquet is the default data source format spark. mergeSchema 默认是false。当设置为true的时候,parquet数据源会合并读取所有的parquet文件的schema,否则会从summary文件或者假如没有summary文件的话随机的选一些数据文件来合并schema。 spark. parquet ("PaymentDetail. peopleDF. Currently, int96-style timestamps are the only known use of the int96 type without an explicit schema-level converted type assignment. All of this work is great, but it can slow things down quite a lot, particularly in Details. To introduce the problem, let's take this code executed with Apache Spark's Scala API: Parquet is a fast columnar data format that you can read more about in two of my other posts: Real Time Big Data analytics: Parquet (and Spark) + bonus and Tips for using Apache Parquet with Spark 2. 问题导读1. parquet ( "Objects. json(" la_json "). conf spark. binaryAsString: false: Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do not differentiate between binary data and strings when writing out the Parquet schema. saveAsTextFile(outputPath); } else if (this. parquet(“people. Spark Schema defines the structure of the data, in other words, it is the structure of the DataFrame, Spark SQL provides StructType & StructField classes to programmatically specify the schema. fs. load(inputFileName) print "Number of partitions: " + str(flightDataDf. load("newFile. concatInputPath(inputPath sqlContext. Below are some advantages of storing data in a parquet format. to_parquet (path[, mode, …]) Write the DataFrame out as a Parquet file or directory. coalesce(this. format("parquet"). getCanonicalPath df. Is schema on write always goodness? Apparently, many of you heard about Parquet and ORC file formats into Hadoop. writeLegacyFormat 默认是false。 Now, if we use Spark to do a similar thing with Orc: scala> val dfOrc = spark. parquet ("people. The basic of reading data in Spark is through DataFrameReader. read_parquet (path[, columns, index_col, …]) Load a parquet object from the file path, returning a DataFrame. load spark. mergeSchema,当其被设置为true时,便会读取所有文件的Schema进行合并。需要注意的是,如果两个文件中存在同一个名称的 . read. parquet ( "AirTraveler. Column type: DECIMAL(19, 0), Parquet schema: optional byte_array col_name [i:2 d:1 r:0] The same query works well in Hive Running queries on parquet data from a spark EMR cluster produces timeout errors. 3. How do I install a parquet file in hive? 4 Answers Find out about the partitioning of your table show partitions users; Copy the table's Parquet files from HDFS to a local directory hdfs dfs -copyToLocal /apps/hive/warehouse/users. In Spark, Parquet data source can detect and merge schema of those files automatically. ᅠ. parquet (this. read. Spark supports text files (compressed), SequenceFiles, and any other Hadoop InputFormat as well as Parquet Columnar storage. Details: You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). The benefit to doing this is you get a slight performance gain by not having to infer the file schema each time the parquet file is read. load("tablee2. /tmp/parquet_schema/"). spark. json", format="json") Parquet Files >>> df3 = spark. Most of the developers are used Avro because it can handle multi-purpose storage format within the Spark and they can be processed with different languages. But it did not. SocketTimeoutException: Read timed out. textFile ("README. read. When reading text-based files from HDFS, Spark can split the files into multiple partitions for processing, depending on the underlying file system. getOrCreate() sc = spark. s3a. Is there a dask equivalent of spark's ability to specify a schema when reading in a parquet file? Possibly using kwargs passed to pyarrow? I have a bunch of parquet files in a bucket but some of the fields have slightly inconsistent names. inputFiles()) # type: ignore input_dirs = set(os. The following command is used to generate a schema by reading the schemaString variable. parquet("path") method. Details. read\ . parquet("/tmp/dataset. As I dictated in the above note, we cant read the parquet data using hadoop cat command. If it can be loaded, infer Unischema from native Parquet schema. Based on the schema we provide in a schema file, the code will format the data accordingly before writing it to the Parquet file. parquet( ). Spark by default supports Parquet in its library hence we don’t need to add any dependency libraries. fs. sql. read. schema. spark. io Fastparquet can read and write int96-style timestamps, as typically found in Apache Spark and Map-Reduce output. read. sql(" select * from la_parquet "), " ts "). # The result of loading a parquet file is also a DataFrame. This is a post to index information related to parquet file format and how Spark can use it. filterPushdown configuration property enabled, buildReaderWithPartitionValues takes the input Spark data source filters and converts them to Parquet filter predicates if possible (as described in the table). e. Unlike the default Apache Spark Parquet writer, it does not require a pre-computed schema or schema that is inferred by performing an extra scan of the input dataset. Spark by default supports Parquet in its library hence we don’t need to add any dependency libraries. split_row_groups (bool, default False) – Divide files into pieces for each row group in the file. parquetFile ("people. You can extend the support for the other files using third party libraries. --conf "spark. The parquet-cpp project is a C++ library to read-write Parquet files. For further information, see Parquet Files. count actualRowCount should not be 0 actualRowCount should be PARQUET only supports schema append whereas AVRO supports a much-featured schema evolution i. mergeSchema. count () # Show a single scala > val df5 = spark. Now that we've established that Parquet is great for querying, let's see how hard it would be to store a copy of our logs as Parquet. e. read. Reading Parquet Files from a Java Application Recently I came accross the requirement to read a parquet file into a java application and I figured out it is neither well documented nor easy to do so. org/download. convertToDelta (spark, "parquet. spark. parquet(*subdirs[:31]). org PySpark supports reading a CSV file with a pipe, comma, tab, space, or any other delimiter/separator files. show () read and write Parquet files, in single- or multiple-file format. Then I work hard to make the schemas of the incoming data consistent so I don’t have to do this too often because it is a pain. files. count // Perform an action on a dataset: return 126 lines scala > textFile. read. parquet(parquetDirectory) As you notice we don’t need to specify any kind of schema, the column names and data types are stored in the parquet files themselves. petastorm. PARQUET is ideal for querying a subset of columns in a multi-column table. Previously I showed how to write parquet files using just parquet library. Assuming, have some knowledge on Apache Parquet file format, DataFrame APIs and basics of Python and Scala. var df=spark. scala > val df = spark . sql. csv (flatInput). The mapping between Avro and Parquet schema This is a short note on how to deal with Parquet files with Spark. printSchema root |-- action: string (nullable = true) |-- timestamp: string (nullable = true) As you saw in the last example Spark inferred type of both columns as strings. Script to add petastorm metadata to an existing parquet dataset. Open a terminal and start the Spark shell with the CData JDBC Driver for Parquet JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Parquet/lib/cdata. orc(“/Data/OpenAddress/austria_partionedByStreet_2_orc") scala> val df = spark. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. etl. When you read the file back, it tells you the schema of the data stored within. read . petastorm_generate_metadata. sql. io. Although Spark SQL itself is not case-sensitive, Hive compatible file formats such as Parquet are. ignoreCorruptFiles to true and then read the files with the desired schema. read. In our example, we will be reading data from csv source. Spark Sql - How can I read Hive table from one user and write a dataframe to HDFS with another user in a single spark sql program asked Jan 6 in Big Data Hadoop & Spark by knikhil ( 120 points) apache-spark Parquet file. Annoying leading underscores in the read parquet format and use In that case we need to create the parquet schema by reading the 1 st set of records before converting it to parquet format(which I will show in my future blog), here in spark library approach also it first read the schema, Because in any case if we need to convert the json or text file to any other format to parquet format, But here we no need to read the records beforehand it just scan the input data and library it self creates the schema out of it, then it covert the input data to parquet If all you need to do is inspect the contents of a parquet file you can do so pretty easily if you already have spark set up like so $ spark-shell scala> val sqlContext = new org. #option1 df=spark. It stores metadata with the data but also a specification of an independent schema for reading the file within the Spark eco-system. parquet, etc. Then I read the temp directory letting spark infer the schema and I end up with a single dataframe I can work with. read. parquet. simpleString. meta) schema_from_file = json. md") // Create a Dataset of lines from a file scala > textFile. xml and also parquet-mr/parquet-tools/pom. secret. option("charset", "UTF8") . save(outputFileName) print "Output: " dbutils. csv("/Data/OpenAddress/austria. map(lambda p: Row(name=p[0],age=int(p[1]))) >>> peopledf = spark. schema Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. But it will trigger schema inference, spark will go over RDD to determine schema that fits the data. A Petastorm dataset can be read into a Spark DataFrame using PySpark, where you can use a wide range of Spark tools to analyze and manipulate the dataset. mode(SaveMode. 1. // Read in the parquet file created above. Alternative to metadata parameter. option ("header", "true"). parquet(“/user/nituser/sales. join(spark. But let’s take a step back and discuss what schema evolution means. format("postgres") > . Use just a Scala case class to define the schema of your data. map { row => println(row) } . dumps (schema)) # Write data to an avro file with open ('users. parquet pyspark options ,spark. tbl_name. load("/tmp/anydir/*") *where anydir have multiple parquet files with different schema. Writing To Parquet: Flat Schema val flatDF = spark. outputSerialization. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). The next benefit is closely related. parquet file for example. parquet ( path ) Details. • It saves storage space and allows for reading individual columns instead of entire files • Advantages: • writing data out to Parquet for long-term storage • Reading from a Parquet file will always be more efficient than JSON or CSV. csv or json ) using inferSchema option. Download Spark Parquet Specify Schema DOC. read. s3a. Parquet file format uses advanced optimizations described in Google’s Dremel paper. Download Spark Parquet Specify Schema PDF. Download and Install maven. rm Oct 22, 2018 · 2 min read In this tutorial I will demonstrate how to process your Event Hubs Capture (Avro files) located in your Azure Data Lake Store using Azure Databricks (Spark). getCanonicalPath val schema = StructType( List( StructField("first_name", StringType, true), StructField("breed", StringType, true) ) ) val df = spark. Hive Metastore in SparkSQL. It natively supports reading and writing data in Parquet, ORC, JSON, CSV, and text format and a plethora of other connectors exist on Spark Packages. contains(). int :: Nil) spark. AnalysisException as below, as the dataframes we are trying to merge has different schema. com/Parquet/parquet-mr. read. streaming. fs. You can set the following Parquet-specific option(s) for reading Parquet files: maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger. Use Spark to read HDFS files with schema. spark. File(". Currently we are using Avro data format in production. When you write a file in these formats, you need to specify your schema. acceleration of both reading and writing using numba Although Spark SQL itself is not case-sensitive, Hive compatible file formats such as Parquet are. x. To fix your error make sure the schema you're defining correctly represents your data as it is stored in the parquet file (i. sql. write. generate_petastorm_metadata (spark, dataset_url, unischema_class=None, use_summary_metadata=False, hdfs_driver='libhdfs3') [source] ¶ Select Download Format Spark Parquet Specify Schema. This means that when you create a table in Athena, it applies schemas when reading the data. 0. Let us read the file that we wrote as a parquet data in above snippet. secret. limit(1). Dump the schema Take sample nation. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. map (r => transformRow (r)). splitSize). option("header", "true"). Csv File Stream. apache. loads (metadata ['avro. org Spark SQL provides support for both reading and writing Parquet files that automatically capture the schema of the original data, It also reduces data storage by 75% on average. schema(schema)as[MyType] List<Row> will be saved to the parquet file as temporary data on HDFS in every fetchsize iteration, where the spark schema which is built using hive meta data will be used: Parquet doesn’t always have built-in support in software other than Spark; it doesn’t support data alteration parquet files are immutable and scheme evolution. read. fs. parquet. Set the Spark property using spark. saveAsTextFile(outputPath); } else if (this. g. Read a Parquet file into a Spark DataFrame. format("parquet") > . As every DBA knows, data definitions can change with time: we may want to add a new column, remove one that is obsolete, or do more complex things, for instance break down one column into multiple columns, like breaking down a string address “1234 Spring schema : an optional pyspark. appName('pyspark - example read csv'). Parquet files maintain the schema along with the data hence it is used to process a structured file. , option("inferSchema", "true") in Scala or csv( , inferSchema="true") in Python). filter ($"IngestTime" > "201903200000") You’d expect Spark (using predicate push-down) to only read the folders needed, and get the schema from those folders. Parquet files maintain the schema along with the data hence it is used to process a structured file. outputSerialization. sql. 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. setAppName("Spark Compaction"); JavaSparkContext sc = new JavaSparkContext(sparkConf); if (this. load("parquet-datasets") // The above is equivalent to the Spark SQL provides support for both reading and writing Parquet files that automatically capture the schema of the original data, It also reduces data storage by 75% on average. Without automatic schema merging, the typical way of handling schema evolution is through historical data reload that requires much work. It also provides the ability to add new columns and merge schemas that do not conflict. In the other side, the schema resolution for schemaless formats, as JSON files, is based on sampling. equals(PARQUET)) { SQLContext sqlContext = new SQLContext (sc); DataFrame parquetFile = sqlContext. read. rdd. read. format ("parquet") Let's now read these two parquet files and compare querying Problem statement and why is this interesting Incoming data is usually in a format different than we would like for long-term storage. parquet”) schema (pyarrow. read. Jul 23, 2020 · In other word, schema evolution allows to update the schema used to write new data while maintaining backwards compatibility with the schema of your old data. tbl_name. write. Row; scala> import org. write. austria_table_orc") Prints out the schema for a given parquet file. 0, provides a unified entry point for programming Spark with the Structured APIs. sql. Posts in csv pyspark script using python and has functionality. {StructType, StructField, StringType}; Generate Schema. Spark SQL must use a case-preserving schema when querying any table backed by files containing Now, we'll create parquet files from the above CSV file using Spark. Set the Spark property using spark. e. csv" ) SparkConf sparkConf = new SparkConf(). format ( "csv" ) . Handling Parquet data types; Reading Parquet Files. read. load(parquetDirectory) #option2 df=spark. we can read either by having a hive table built on top of parquet data or use spark command to read the parquet data. Using toDF By importing spark sql implicits, one can create a DataFrame from a local Seq, Array or RDD, as long as the contents are of a Product sub-type (tuples and case classes are well-known examples of Product sub-types). tsv" flightDataDf. read. Rather than creating Parquet schema and using ParquetWriter and ParquetReader to write and read file respectively it is more convenient to use a framework like Avro to create schema. See full list on animeshtrivedi. jdbc. You can use generic records if you don't want to use the case class, too. The partitions for the old files with the now-incorrect schemas are still there. Out of several good points using Avro, we know that it is good in schema evolution. conf. Spark Parquet reader is used to read data. Read API structure. Compatible with files generated with Apache Spark inputFileName = "/FileStore/tables/data/flight-data/csv/2015_summary. write. Hadoop Distributed File System is the classical example of the schema on read system. execute SQL over tables, cache tables, and read parquet files. Recent versions of Sqoop can produce Parquet output files using the --as-parquetfile option. We look in the method of reading parquet file using spark command. parquet("/tmp/dataset_unpartitioned. read. apache. binaryAsString when writing Parquet files through Spark. Stream data from Kafka > > val stream = KafkaUtils. You can read more about the parquet file format on the Apache Parquet Website. Files that don’t match the specified schema are ignored. They can be created from local lists, distributed RDDs or reading from datasources. parquet. concatInputPath(inputPath)); textFile. 0787694356 | | snx001|2020-01-09| [ [1, 4. first // First item in the Dataset scala > val linesWithSpark = textFile. createDirectStream > > 1. PySpark Pyspark SQL provides methods to read Parquet file into DataFrame and write DataFrame to Parquet files, parquet () function from DataFrameReader and DataFrameWriter are used to read from and write/create a Parquet file respectively. ls(outputFileName) #Cleanup #dbutils. readStream (). _jsparkSession. 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. BytesIO object, as long as you don’t use partition_cols, which creates multiple files. s3a. apache. Find the Parquet files and rewrite them with the correct schema. As you can see Spark did a lot of work behind the scenes: it read each line from the file, deserialized the JSON, inferred a schema, and merged the schemas together into one global schema for the whole dataset, filling missing values with null when necessary. parquet") //Parquet files can also be registered as tables and then used in SQL statements. Parquet is a popular column-oriented storage format that can store records with nested fields efficiently. parquet function that returns an RDD of JSON strings using the column names and schema to produce adult_df = spark. json("table. deepcopy (reader. read. Avro did not perform well when processing the entire dataset, as The Spark jobs, which are responsible for processing and transformations, read the data in its entirety and do little to no filtering. sql Inferred from Data: If the data source does not have a built-in schema (such as a JSON file or a Python-based RDD containing Row objects), Spark tries to deduce the DataFrame schema based on the input data. xml to one valid version, for example: <version>1. But Spark SQL has built-in support for Parquet data format, which makes processing data in parquet files easy using simple DataFrames API. They will be automatically converted to times upon loading. fs. option ("header",true). builder. The convention used by Spark to write Parquet data is configurable. val smallDf = spark. SparkConf sparkConf = new SparkConf(). When a read of Parquet data occurs, Drill loads only the necessary columns of data, which reduces I/O. The schema can either be a Spark StructType, or a DDL-formatted string like col0 INT, col1 DOUBLE. Show help manual cd target java -jar parquet-tools-1. In addition to these features, Apache Parquet supports limited schema evolution, i. generate_petastorm_metadata (spark, dataset_url, unischema_class=None, use_summary_metadata=False, hdfs_driver='libhdfs3') [source] ¶ When writing a DataFrame as Parquet, Spark will store the frame's schema as metadata at the root of the directory. parquet") scala > val df6 = spark . sparkContext >>> lines = sc. load(parquetFilesPath) // read the parquet files > . read. textFile("people. Jobs With this article, I will start a series of short tutorials on Pyspark, from data pre-processing to modeling. The fix is to modify parquet-mr/pom. read. Since this is a small program, we will be using Spark shell instead of writing a full fledged Spark code. avro, spark. The parquet-rs project is a Rust library to read-write Parquet files. With schema-on-read, you’re not tied to a predetermined structure so you can present the data back in a schema that is most relevant to the task at hand. index_colstr or list of str, optional, default: None As a rule of thumb we can consider that every time when some schema information is available (e. sql. parquet(*subdirs[1:32]). conf. These Parquet preserves the schema of the data. read. Since there are already many tutorials to perform various operations in the context, this post mainly consolidate the links. load(dst) . format("parquet"). Files that don’t match the specified schema are ignored. Reading ORC files in Spark. select ("customers"). read json data to json dataframe: > > stream. write. from pyspark. schema. _ val csvPath = new java. The first step that we usually do is transform the data into a format such as Parquet that can easily be queried by Hive/Impala. sql. One is by using the dataframe API and other one is to run a select query in hive table built on top of parquet format file. pyspark will not try to reconcile differences between the schema you provide and what the actual types are in the data and an exception will be thrown. spark. builder. schema(dstSchema) . Parquet can be read and written using the Avro API and Avro Schema (which gives the idea of storing all raw data in the Avro format, but all processed data in parquet); It also provides predicate pushdown , thus reducing the further cost of transferring data from storage to the processing engine for filtering; How to build and use parquet-tools to read parquet files. schema(schema). It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. parquet. s3a. Spark SQL provides support for both reading and writing Parquet files that automatically capture the schema of the original data, It also reduces data storage by 75% on average. For information about Parquet, see Using Apache Parquet Data Files with CDH. parquet (flatOutput) 19. csv") scala> df. parquet overwrite pyspark ,pyspark open parquet file ,spark output parquet ,pyspark parquet partition ,pyspark parquet python ,pyspark parquet to pandas ,pyspark parquet read partition ,pyspark parquet to pandas For a query like spark. schema. sql import SparkSession spark = SparkSession. sql ( "SELECT * FROM object" ) scala> allrecords. parquet (parquetPath) Let’s view the contents of the Parquet lake. json(dataRdd) > > 2. read. schema val df = spark. As structured streaming extends the same API, all those files can be read in the streaming also. The Apache Parquet project provides a standardized open-source columnar storage format for use in data analysis systems. parquet ( dataset_url ) # Show a schema dataframe . You can either define the schema programmatically as part of the read operation as demonstrated in this section, or let Spark infer the schema as outlined in the Spark SQL and DataFrames documentation (e. sql. rea. read_parquet ('df. we concentrate on five different format of data, namely, Avro, parquet, json, text, csv. I tried converting directly from Avro to Spark Row, but somehow that did not work. read. choice of compression per-column and various optimized encoding schemes; ability to choose row divisions and partitioning on write. Designed for both batch and stream processing, it also addresses concerns regarding system complexity. Spark is more flexible in this regard compared to Hadoop: Spark can read data directly from MySQL, for example. parquet. files. The resultant dataset contains only data from those files that match the specified schema. read. option("mergeSchema",true). The resultant dataset contains only data from those files that match the specified schema. save(“sales_parquet”) Create dataframe on top of Parquet file using below command: val sales = sqlContext. schema. read. createDataFrame(people) Registering DataFrames as ViewsSpecify Schema import org. A schema helps impose structure on data and enables easy data querying and filtering. getOrCreate() if isinstance(schema, StructType): jschema = spark. More details about Schema on Read and Schema on Write approach you could find here. When reading text-based files from a local file system, Spark creates one partition for each file being read. option ( "header" , true ) . by reading it in as an RDD and converting it to a dataframe after pre-processing it Let’s specify schema for the ratings dataset. Writing a Parquet Index. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). val parquetFile = sqlContext. read (). spark read parquet with schema