Spark read parquet with different schema

Details. You can read data from HDFS ( hdfs:// ), S3 ( s3a:// ), as well as the local file system ( file:// ). If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults.conf spark.hadoop.fs.s3a.access.key, spark.hadoop.fs.s3a.secret.key or any of the methods outlined in the aws-sdk documentation Working with ...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. Below are some advantages of storing data in a parquet format. Spark by default supports Parquet in its library hence we don't need to add any dependency libraries. gospel choir chords Jun 11, 2020 · Apache Spark in Azure Synapse Analytics enables you easily read and write parquet files placed on Azure storage. Apache Spark provides the following concepts that you can use to work with parquet files: DataFrame.read.parquet function that reads content of parquet file using PySpark DataFrame.wri... Re: Met a problem when using spark to load parquet files with different version schemas. Wei Yan Mon, 11 May 2015 20:26:45 -0700. Creating dataframes and union them looks reasonable. thanks, Wei. On Mon, May 11, 2015 at 6:39 PM, Michael Armbrust <[email protected]> wrote: > Ah, yeah sorry. I should have read closer and realized that what ...How to unify schema when writing to parquet in apache spark? 我有一个模式,例如:我用来读取的: val DF = spark.read.schema(A.schema).json(inputPath) 现在我现在有一个不同的架构,假设d是一个+ b + c的联盟。写入 parquet 时,我想确保数据框架用模式d写入木条地 Board 上。 opening act hollywood bowl voting The data was generated in Parquet format in the following partitions and each partition has 10 rows and a different schema: ... raw/test_data_parquet" df = spark.read.schema(schema).option ... malayalam movies 2022 Jun 11, 2020 · Apache Spark in Azure Synapse Analytics enables you easily read and write parquet files placed on Azure storage. Apache Spark provides the following concepts that you can use to work with parquet files: DataFrame.read.parquet function that reads content of parquet file using PySpark DataFrame.wri... Problem. Let’s say you have a large list of essentially independent Parquet files, with a variety of different schemas. You want to read only those files that match a specific …Let's see what happens when we force the desired schema when reading the parquet files. ... When we ask the data frame to return a sample of the lines ( df.show() ) ... perfect game super25 national championshipSpark has easy fluent APIs that can be used to read data from JSON file as DataFrame object. In this code example, JSON file named 'example.json' has the following content: In the code snippet, the following option is important to let Spark to handle multiple line JSON content:. The Schema Registry is running on port 8081. It offers a REST API ...Re: Met a problem when using spark to load parquet files with different version schemas. > > BTW, I use spark 1.3.1, and already set > "spark.sql.parquet.useDataSourceApi" to false. >. Schema merging is only supported when this flag is set to true (setting it to false uses old code that will be removed once the new code is proven). arriva 56 bus route Save the contents of a SparkDataFrame as a Parquet file, preserving the schema. Files written out with this method can be read back in as a SparkDataFrame using read.parquet(). Save the contents of SparkDataFrame as a Parquet file, preserving the schema. — write.parquet • SparkR Later on, you can quickly navigate to a particular database using this command: [[email protected] ~]# mysql-u root -p mydatabase < radius.sql.Delete a MySQL Database. To get rid of a database just type: DROP DATABASE dbName.Dec 21, 2020 · The data was generated in Parquet format in the following partitions and each partition has 10 rows and a different schema: ... raw/test_data_parquet" df = spark.read.schema(schema).option ... Spark SQL provides StructType & StructField classes to programmatically specify the schema. Spark Read JSON with schema Use the StructType class to create a custom schema, below we initiate this class and use add a method to add columns to it by providing the column name, data type and nullable option.When investigating a write to Parquet, there are two options: Use a manually defined schema on an establish DataFrame schema = types.StructType ( [ types.StructField ("index", types.LongType...Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When reading Parquet files, all columns are automatically converted to be nullable for compatibility reasons.Hi, devs, I met a problem when using spark to read to parquet files with two different versions of schemas. For example, the first file has one field with "int" type, while the same field in the second file is a "long". I thought spark would automatically generate a merged schema "long", and use that schema to process both files. the ultimate medical scribe handbook pdf May 11, 2015 · Hi, devs, I met a problem when using spark to read to parquet files with two different versions of schemas. For example, the first file has one field with "int" type, while the same field in the second file is a "long". I thought spark would automatically generate a merged schema "long", and use that schema to process both files. Jun 11, 2020 · Apache Spark in Azure Synapse Analytics enables you easily read and write parquet files placed on Azure storage. Apache Spark provides the following concepts that you can use to work with parquet files: DataFrame.read.parquet function that reads content of parquet file using PySpark DataFrame.wri... By passing path/to/table to either SparkSession.read.parquet or SparkSession.read.load, Spark SQL will automatically extract the partitioning information from the paths. ... In this way, users may end up with multiple Parquet files with different but mutually compatible schemas. higan vs bsnes 2 Answers. Since you're using spark to read parquet file, one of the advantages is that you can use schema-on-read on the fly approach, which means that you can declare the schema when you read the data. You can: schema = types.StructType ( [ types.StructField ('date', types.TimestampType ()), ... # declartion of other columns ]) testDF = spark ...All Languages >> Python >> using parquet files in pyspark "using parquet files in pyspark " Code Answer. parquet pyspark . python by Sore Stork on Sep 19 2021 Comment . 0. Add a Grepper Answer . Answers related to "using parquet files in pyspark ". ...Aug 20, 2020 · Couple approaches on how we overcame parquet schema related issues when using Pandas and Spark dataframes. In my current project we rely solely on parquet files for all our data processing. corfield doctors surgery Spark . an open source cluster computing framework that provides an interface for entire programming clusters with implicit data parallelism and fault-tolerance. serves as 'general-purpose' and 'fast cluster computing platform'. - runs computations in memory & provides a quicker system for complex applications operating on disk.· Step 3.1 : Load into dataframe: Now we will load the files in to spark dataframe , here we are considering that all the files present in the directory have same schema . It means. Sylvia Walters never planned to be in the food-service business. In fact, before she started Sylvia's Soul Plates in April, Walters was best known for fronting the ...2020. 4. 29. · Step 3.1 : Load into dataframe: Now we will load the files in to spark dataframe , here we are considering that all the files present in the directory have same schema . It means. quest diagnostic near me 2 Answers. Since you're using spark to read parquet file, one of the advantages is that you can use schema-on-read on the fly approach, which means that you can declare the schema when you read the data. You can: schema = types.StructType ( [ types.StructField ('date', types.TimestampType ()), ... # declartion of other columns ]) testDF = spark ...use hadoop FileSystem to get all parquet file paths in a Seq map over the Seq with spark.read.parquet. You will now have a Seq of individual DataFrames fold left on your Seq if df1 schema == df2 schema, then return df1.union (df2) else perform required transformations (casting, column reordering, etc) and then union both df together gated communities in las vegas for sale Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons.Reading Parquet files into a DataFrame. Parquet files are stored in a directory structure that contains the data files, metadata, a number of compressed files, and some status files. Metadata in the footer contains the version of the file format, the schema, and column data such as the path, etc. For example, a directory in a Parquet file might.Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons.Solution. Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don’t match the specified schema are ignored. The resultant dataset contains only data from those files that match the specified schema. Set the Spark property using spark.conf.set: Bash.Reading Parquet files into a DataFrame. Parquet files are stored in a directory structure that contains the data files, metadata, a number of compressed files, and some status files. Metadata in the footer contains the version of the file format, the schema, and column data such as the path, etc. For example, a directory in a Parquet file might. npc lackland classic 2022 The data was generated in Parquet format in the following partitions and each partition has 10 rows and a different schema: ... raw/test_data_parquet" df = spark.read.schema(schema).option ...2 Answers. Since you're using spark to read parquet file, one of the advantages is that you can use schema-on-read on the fly approach, which means that you can declare the schema when you read the data. You can: schema = types.StructType ( [ types.StructField ('date', types.TimestampType ()), ... # declartion of other columns ]) testDF = spark ... how many gb is davinci resolve Jan 13, 2021 ... Parquet is a very popular column based format. Spark can automatically filter useless data using parquet file statistical data by pushdown ...May 30, 2022 · Using Spark, you can convert Parquet files to CSV format as shown below. df = spark. read. parquet("/path/to/infile.parquet") df. write. csv("/path/to/outfile.csv") Is parquet smaller than CSV? Uncompressed CSV file: The uncompressed CSV file has a total size of 4 TB. ... 2 Answers. Since you're using spark to read parquet file, one of the advantages is that you can use schema-on-read on the fly approach, which means that you can declare the schema when you read the data. You can: schema = types.StructType ( [ types.StructField ('date', types.TimestampType ()), ... # declartion of other columns ]) testDF = spark ... used mahindra tractors for sale australia 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. Below are some advantages of storing data in a parquet format. Spark by default supports Parquet in its library hence we don't need to add any dependency libraries. why is dan guthrie leaving fox 17 nashville We can directly use convert_dtypes() on the Pandas dataframe when saving the parquet file and it would store the data and datatype correctly. Then we simply read it in Spark and apply our …Hi, devs, I met a problem when using spark to read to parquet files with two different versions of schemas. For example, the first file has one field with "int" type, while the same field in the second file is a "long". I thought spark would automatically generate a merged schema "long", and use that schema to process both files.Mar 11, 2022 · Solution. Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don’t match the specified schema are ignored. The resultant dataset contains only data from those files that match the specified schema. Set the Spark property using spark.conf.set: Bash. gwinnett county police jobs Jun 11, 2020 · Apache Spark in Azure Synapse Analytics enables you easily read and write parquet files placed on Azure storage. Apache Spark provides the following concepts that you can use to work with parquet files: DataFrame.read.parquet function that reads content of parquet file using PySpark DataFrame.wri... Save the contents of a SparkDataFrame as a Parquet file, preserving the schema. Files written out with this method can be read back in as a SparkDataFrame using read.parquet(). Save the contents of SparkDataFrame as a Parquet file, preserving the schema. — write.parquet • SparkR model airplane gas engines for sale use hadoop FileSystem to get all parquet file paths in a Seq map over the Seq with spark.read.parquet. You will now have a Seq of individual DataFrames fold left on your Seq if df1 schema == df2 schema, then return df1.union (df2) else perform required transformations (casting, column reordering, etc) and then union both df togetherParquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When reading Parquet files, all columns are automatically converted to be nullable for compatibility reasons. petite porn stars If you are reading from a secure S3 bucket be sure to set the following in your spark -defaults.conf spark .hadoop.fs.s3a.access.key, spark .hadoop.fs.s3a.secret.key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work.We can directly use convert_dtypes() on the Pandas dataframe when saving the parquet file and it would store the data and datatype correctly. Then we simply read it in Spark and apply our …May 01, 2021 · To do that, execute this piece of code: json_df = spark.read.json (df.rdd.map (lambda row: row.json)) json_df.printSchema JSON schema. Note: Reading a collection of files from a path ensures that a global schema is captured over all the records stored in those files.. "/>Save the contents of a SparkDataFrame as a Parquet file, preserving the schema. Files written out with this method can be read back in as a SparkDataFrame using read.parquet(). Save the contents of SparkDataFrame as a Parquet file, preserving the schema. — write.parquet • SparkR can corroded brass be restored May 31, 2022 ... Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don't match ...Find helpful customer reviews and review ratings for Urth Lens Mount Adapter: Compatible with Leica M Lens to Fujifilm X Camera Body at Amazon.co.jp. Read honest and unbiased product reviews from our users.Within our Camera Lens Adapters category, this gadget seller lists 588 various tech goods. On this marketplace, you can snap up this nifty piece of kit for sale from 1 …About Json With Read Spark Schema Different . The generated schema can be used when loading json data Parse a given json - schema file by providing the path to the input file. to make code injection from untrusted schemas impossible, code generation is fully re-written to be safe and to allow code optimization (compiled schema code size is.Spark . an open source cluster computing framework that provides an interface for entire programming clusters with implicit data parallelism and fault-tolerance. serves as 'general-purpose' and 'fast cluster computing platform'. - runs computations in memory & provides a quicker system for complex applications operating on disk. achd traffic cameras Using Spark, you can convert Parquet files to CSV format as shown below. df = spark. read. parquet("/path/to/infile.parquet") df. write. csv("/path/to/outfile.csv") Is parquet smaller than CSV? Uncompressed CSV file: The uncompressed CSV file has a total size of 4 TB. ... southern lacrosse tournaments Let's look at a withSum transformation that adds the num1 and num2 columns in a DataFrame. def withSum (df: DataFrame. by using the Spark SQL read function such as spark.read.csv, spark.read.json, spark.read.orc, spark.read.avro, spark.rea.parquet, etc. by reading it in as an RDD and converting it to a dataframe after pre-processing it Let's ...Hi, devs, I met a problem when using spark to read to parquet files with two different versions of schemas. For example, the first file has one field with "int" type, while the same field in the second file is a "long". I thought spark would automatically generate a merged schema "long", and use that schema to process both files.The data was generated in Parquet format in the following partitions and each partition has 10 rows and a different schema: ... raw/test_data_parquet" df = spark.read.schema(schema).option ... what rpm should i run my yamaha outboard Save the contents of a SparkDataFrame as a Parquet file, preserving the schema. Files written out with this method can be read back in as a SparkDataFrame using read.parquet(). Save the contents of SparkDataFrame as a Parquet file, preserving the schema. — write.parquet • SparkRBy processing the file with the spark.read.parquet, the Spark SQL automatically extracts the information, and the schema is returned. The Data Type is inferred automatically. The schema can be merged by enabling the mergeSchema to True while reading the parquet File. The is how we can read the Parquet file in PySpark.Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons. can you ignore a camera ticketMay 31, 2022 · Solution. Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don’t match the specified schema are ignored. The resultant dataset contains only data from those files that match the specified schema. Set the Spark property using spark.conf.set: Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When reading Parquet files, all columns are automatically converted to be nullable for compatibility reasons. abandoned places in flint michigan With schema evolution, one set of data can be stored in multiple files with different but compatible schema. In Spark, Parquet data source can detect and merge schema of …Re: Met a problem when using spark to load parquet files with different version schemas. Wei Yan Mon, 11 May 2015 20:26:45 -0700. Creating dataframes and union them looks reasonable. thanks, Wei. On Mon, May 11, 2015 at 6:39 PM, Michael Armbrust <[email protected]> wrote: > Ah, yeah sorry. I should have read closer and realized that …The VectorizedColumnReader constructor takes a ColumnDescriptor (can be found in the schema) and a PageReader (can be found from the rowGroup , a Parquet API ... through the griffinscope How to unify schema when writing to parquet in apache spark? 我有一个模式,例如:我用来读取的: val DF = spark.read.schema(A.schema).json(inputPath) 现在我现在有一个不同的架构,假设d是一个+ b + c的联盟。写入 parquet 时,我想确保数据框架用模式d写入木条地 Board 上。Nov 04, 2022 · 2 Answers. Since you're using spark to read parquet file, one of the advantages is that you can use schema-on-read on the fly approach, which means that you can declare the schema when you read the data. You can: schema = types.StructType ( [ types.StructField ('date', types.TimestampType ()), ... # declartion of other columns ]) testDF = spark ... May 31, 2022 · Solution Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don’t match the specified schema are ignored. The resultant dataset contains only data from those files that match the specified schema. Set the Spark property using spark.conf.set: dt466 high pressure oil pump location Parquet files are easier to work with because they are supported by so many different projects. Parquet stores the file schema in the file metadata. CSV files don't store file metadata, so readers need to either be supplied with the schema or the schema needs to be inferred.Its native format is Parquet, hence it supports parallel operations and it is fully compatible with Spark.. Sep 10, 2021 · Copy activity supports resume from last failed run when you copy large size of files as-is with binary format between file-based stores and choose to preserve the folder /file hierarchy from source to sink, e.g. to migrate data from Amazon S3 to Azure Data Lake Storage …Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When reading Parquet files, all columns are automatically converted to be nullable for compatibility reasons.Nov 04, 2022 · 2 Answers. Since you're using spark to read parquet file, one of the advantages is that you can use schema-on-read on the fly approach, which means that you can declare the schema when you read the data. You can: schema = types.StructType ( [ types.StructField ('date', types.TimestampType ()), ... # declartion of other columns ]) testDF = spark ... free wind band music pdf use hadoop FileSystem to get all parquet file paths in a Seq map over the Seq with spark.read.parquet. You will now have a Seq of individual DataFrames fold left on your Seq if df1 schema == df2 schema, then return df1.union (df2) else perform required transformations (casting, column reordering, etc) and then union both df togetherRead a Parquet file into a Spark DataFrame. Usage spark_read_parquet( sc, name = NULL, path = name, options = list(), repartition = 0, memory = TRUE, overwrite = TRUE, columns = NULL, schema = NULL, ... ) Arguments Details You can read data from HDFS ( hdfs:// ), S3 ( s3a:// ), as well as the local file system ( file:// ).dask.dataframe.to_parquet(df, path, engine='auto', compression='snappy', ... use a different schema dependent on the dtypes of the corresponding partition.Apr 27, 2017 · Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with a simple schema, and gradually add more columns to the schema as needed. In this way, users may end up with multiple Parquet files with different but mutually compatible schemas. The Parquet data source is now able to automatically detect this ... macungie stabbing Org apache spark sparkexception failed merging schema. Oct 19, 2022 m1 closures tonight unity terrain decals. GitHub user ArunkumarRamanan opened a pull request: ... (Please. marcus gm card best PPC blogs final_df= spark.sql("""select _Dishtv,_Cid,_name, Channel,case when tune_type = 'true' then null else channeltune end as ... 5 bedroom holiday house st ives Nov 28, 2017 · When investigating a write to Parquet, there are two options: Use a manually defined schema on an establish DataFrame schema = types.StructType ( [ types.StructField ("index", types.LongType... 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. Below are some advantages of storing data in a parquet format. Spark by default supports Parquet in its library hence we don’t need to add any dependency libraries.In my case, the main bottle-neck was moving data inside AWS (from S3 to spark nodes) df.write.mode('overwrite').format('parquet').saveAsTable(new_name) #change parquet to jsonsonce When your job is finished look at the hive directory for above table and see how many files are 0 sized.Spark SQL and DataFrames: Introduction to Built-in Data Sources In the … wqad morning news team Nov 28, 2017 · Creating a DataFrame from a Parquet filepath is easy for the user. It can be done by calling either SparkSession.read.parquet () or SparkSession.read.load ('path/to/data.parquet') which ... Read a Parquet file into a Spark DataFrame. Usage spark_read_parquet( sc, name = NULL, path = name, options = list(), repartition = 0, memory = TRUE, overwrite = TRUE, columns = NULL, schema = NULL, ... ) Arguments Details You can read data from HDFS ( hdfs:// ), S3 ( s3a:// ), as well as the local file system ( file:// ). Examples · Single-File Reads · Multi-File Reads and Globs · Partial Reading · Inserts and Views · Parquet Metadata · Parquet Schema · Writing to Parquet Files. cold offer biglaw Unable to read Spark Iceberg from S3 - ClassCastException: class parquet.schema.MessageType cannot be cast to Iceberg parquet.schema.MessageType Ask Question Asked todayNov 28, 2017 · Creating a DataFrame from a Parquet filepath is easy for the user. It can be done by calling either SparkSession.read.parquet () or SparkSession.read.load ('path/to/data.parquet') which ... Org apache spark sparkexception failed merging schema. Oct 19, 2022 m1 closures tonight unity terrain decals. GitHub user ArunkumarRamanan opened a pull request: ... (Please. marcus gm card best PPC blogs final_df= spark.sql("""select _Dishtv,_Cid,_name, Channel,case when tune_type = 'true' then null else channeltune end as ...Unable to read Spark Iceberg from S3 - ClassCastException: class parquet.schema.MessageType cannot be cast to Iceberg parquet.schema.MessageType Ask Question Asked today spin the wheel of truth When investigating a write to Parquet, there are two options: Use a manually defined schema on an establish DataFrame schema = types.StructType ( [ types.StructField ("index", types.LongType...2020. 4. 29. · Step 3.1 : Load into dataframe: Now we will load the files in to spark dataframe , here we are considering that all the files present in the directory have same schema . It means.We can directly use convert_dtypes() on the Pandas dataframe when saving the parquet file and it would store the data and datatype correctly. Then we simply read it in Spark and apply our …One possible cause: Parquet column cannot be converted in the corresponding files Caused by: org.apache.parquet.io.ParquetDecodingException: Can not read value at 1 in block 0 in file file:/home ... bulk buying for resale uk Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons. Solution. Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don’t match the specified schema are ignored. The resultant dataset contains only data from those files that match the specified schema. Set the Spark property using spark.conf.set:dataFrameReader = spark.read print "Type: " + str(type(dataFrameReader)) ... spark.read\ .format("parquet")\ .load(inputFileName) print "Schema" ...1 Answer Sorted by: 7 Move .schema () before .parquet () then spark will read the parquet file with the specified schema df2 = spark.read.schema (df1.schema).parquet (load_path2) Share Improve this answer Follow answered Feb 25, 2020 at 18:26 notNull 26.9k 2 24 46 Add a comment Your Answer cogic sunday school lesson this week 2022 When investigating a write to Parquet, there are two options: Use a manually defined schema on an establish DataFrame schema = types.StructType ( [ types.StructField ("index", types.LongType...Solution. Set the Apache Spark property spark.sql.files.ignoreCorruptFiles to true and then read the files with the desired schema. Files that don’t match the specified schema are ignored. The resultant dataset contains only data from those files that match the specified schema. Set the Spark property using spark.conf.set: Bash. scrap metal prices toledo When we read multiple Parquet files using Apache Spark, we may end up with a problem caused by schema differences. When Spark gets a list of files to read, it picks the schema from either the Parquet summary file or a randomly chosen input file: Most likely, you don't have the Parquet summary file because it is not a popular solution.Similar to write, DataFrameReader provides parquet() function (spark.read.parquet) to read the parquet files and creates a Spark DataFrame. In this example ... baseball at the beach brunswick ga 2022 Re: Met a problem when using spark to load parquet files with different version schemas. Wei Yan Mon, 11 May 2015 20:26:45 -0700. Creating dataframes and union them looks reasonable. thanks, Wei. On Mon, May 11, 2015 at 6:39 PM, Michael Armbrust <[email protected]> wrote: > Ah, yeah sorry. I should have read closer and realized that what ...You have two ways to create orc tables from spark (compatible with hive). I tested codes below with hdp 2.3.2 sandbox and spark 1.4.1. 1- Saving orc file from spark and create table directly on hive, see this code: spark -shell --master yarn-client --driver-memory 512m --executor.use hadoop FileSystem to get all parquet file paths in a Seq map over the Seq with spark.read.parquet. You will now have a Seq of individual DataFrames fold left on your Seq if df1 schema == df2 schema, then return df1.union (df2) else perform required transformations (casting, column reordering, etc) and then union both df together piercing shops in downtown long beach