if use the same ORC but use hive to create a table using second query even then I am getting the same error. But I use an existing table alter table with a new coulmn using the Spark Hive context and save as ORC with snappy compression, I am getting the following error ORC does not support type conversion from STRING to VARCHAR. ![]() if I store ORC file with snappy compression and use hive to create table using script 1 then it is working fine. If use the first script using spark sql and store the file as ORC with snappy compression it is working. The original table create when we scooped the data from SQL server using SQOOP importĬREATE TABLE `testtabledim`( `person_key` bigint, `pat_last` varchar(35), `pat_first` varchar(35), `pat_dob` timestamp, `pat_zip` char(5), `pat_gender` char(1), `pat_chksum1` bigint, `pat_chksum2` bigint, `dimcreatedgmt` timestamp, `pat_mi` char(1), `h_keychksum` string, `patmd5` string) ROW FORMAT SERDE '.ql.io.orc.OrcSerde' STORED AS INPUTFORMAT '.ql.io.orc.OrcInputFormat' OUTPUTFORMAT '.ql.io.orc.OrcOutputFormat' LOCATION 'hdfs://hdp-cent7-01:8020/apps/hive/warehouse/datawarehouse.db/testtabledim' TBLPROPERTIES ( 'COLUMN_STATS_ACCURATE'='false', 'last_modified_by'='hdfs', 'last_modified_time'='1469026541', 'numFiles'='1', 'numRows'='-1', 'orc.compress'='SNAPPY', 'rawDataSize'='-1', 'totalSize'='11144909', 'transient_lastDdlTime'='1469026541') You can find it from here.A) The following is the show create table testtable results ( this table is created with Spark SQLĬREATE TABLE `testtabletmp1`( `person_key` bigint, `pat_last` string, `pat_first` string, `pat_dob` timestamp, `pat_zip` string, `pat_gender` string, `pat_chksum1` bigint, `pat_chksum2` bigint, `dimcreatedgmt` timestamp, `pat_mi` string, `h_keychksum` string, `patmd5` string) ROW FORMAT SERDE '.ql.io.orc.OrcSerde' STORED AS INPUTFORMAT '.ql.io.orc.OrcInputFormat' OUTPUTFORMAT '.ql.io.orc.OrcOutputFormat' LOCATION 'hdfs://hdp-cent7-01:8020/apps/hive/warehouse/datawarehouse.db/testtabledimtmp1' | TBLPROPERTIES ( 'orc.compress'='SNAPPY', 'transient_lastDdlTime'='1469207216')Ģ. To install Visual C++ 2010 Redistributable Package: Visual C++ 2010 Redistributable Package is not installed with self-hosted IR installations.Package the jvm.dll with all other required assemblies of OpenJDK into Self-hosted IR machine, and set system environment variable JAVA_HOME accordingly. To use OpenJDK: It's supported since IR version 3.13.To use JRE: The 64-bit IR requires 64-bit JRE.Note currently Copy activity doesn't support LZO when read/write ORC files.īelow is an example of ORC dataset on Azure Blob Storage: \JavaHome) for JRE, if not found, secondly checking system variable JAVA_HOME for OpenJDK. Supported types are none, zlib, snappy (default), and lzo. When reading from ORC files, Data Factories automatically determine the compression codec based on the file metadata. The compression codec to use when writing to ORC files. See details in connector article -> Dataset properties section. ![]() Each file-based connector has its own location type and supported properties under location. The type property of the dataset must be set to Orc. This section provides a list of properties supported by the ORC dataset. Dataset propertiesįor a full list of sections and properties available for defining datasets, see the Datasets article. ![]() ORC format is supported for the following connectors: Amazon S3, Amazon S3 Compatible Storage, Azure Blob, Azure Data Lake Storage Gen1, Azure Data Lake Storage Gen2, Azure Files, File System, FTP, Google Cloud Storage, HDFS, HTTP, Oracle Cloud Storage and SFTP. Learn how to start a new trial for free!įollow this article when you want to parse the ORC files or write the data into ORC format. Microsoft Fabric covers everything from data movement to data science, real-time analytics, business intelligence, and reporting. Try out Data Factory in Microsoft Fabric, an all-in-one analytics solution for enterprises.
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