Spark SQL on ORC files doesn't return correct Schema Column names Ask Question Asked 3 years, 2 months ago. Active 1 year, 5 months ago. Viewed 5k times 3. I have a directory containing ORC files. I am creating a DataFrame using the below code. var data. Currently if one is trying to query ORC tables in Hive, the plan generated by Spark hows that its using the `HiveTableScan` operator which is generic to all file formats. We could instead use the ORC data source for this so that we can get ORC specific optimizations like predicate pushdown. Current behaviour. This PR updates PR 6135 authored by @zhzhan from Hortonworks. This PR implements a Spark SQL data source for accessing ORC files. NOTE Although ORC is now an Apache TLP, the codebase is still tightly coupled with Hive. That's why the new ORC data source is under org.apache.spark.sql.hive package, and must be used with HiveContext. However, it. Using Hive and ORC with Apache Spark. In this tutorial, we will explore how you can access and analyze data on Hive from Spark. Spark SQL uses the Spark engine to execute SQL queries either on data sets persisted in HDFS or on existing RDDs.
SPARK-16628 OrcConversions should not convert an ORC table represented by MetastoreRelation to HadoopFsRelation if metastore schema does not match schema stored in ORC files Resolved links to. I have an external hive table stored as partitioned orc file see the table schema below. I tried to query from the table with where clause> hiveContext.setConf"spark.sql.orc.filterPushdown", "true" hiveContext.sql"select u, v from 4D where zone = 2 and x = 320 and y = 117". Spark: Save Dataframe in ORC format. Ask Question Asked 4 years, 2 months ago. Active 4 years, 2 months ago. Viewed 9k times 7. In the previous. Browse other questions tagged scala apache-spark apache-spark-sql orc or ask your own question. Blog We. Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do not differentiate between binary data and strings when writing out the Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems. 1.spark sql简介spark sql是为了处理结构化数据的一个spark 模块。不同于spark rdd的基本API，spark sql接口更多关于数据结构本身与执行计划等更多信息。. 解决SparkSql 读取parquet或者Orc.
What changes were proposed in this pull request? This PR adds an ORC columnar-batch reader to native OrcFileFormat. Since both Spark ColumnarBatch and ORC RowBatch are used together, it is faster than the current Spark implementation. This replaces the prior PR, 17924. Also, this PR adds OrcReadBenchmark to show the performance improvement.
Developing Spark SQL Applications; Fundamentals of Spark SQL Application Development SparkSession — The Entry Point to Spark SQL Builder — Building SparkSession using Fluent API implicits Object — Implicits. SPARK-18355 Spark SQL fails to read data from a ORC hive table that has a new column added to it. Resolved; SPARK-19109 ORC metadata section can sometimes exceed protobuf message size limit. Resolved; SPARK-19430 Cannot read external tables with VARCHAR columns if they're backed by ORC files written by Hive 1.2.1. 16/07/2015 · Joint Blog Post: Bringing ORC Support into Apache Spark. July 16, 2015 by Zhan Zhang, Cheng Liang and Patrick Wendell Posted in Engineering Blog July 16, 2015. Currently, the code for Spark SQL ORC support is under package org.apache.spark.sql.hive and must be used together with Spark SQL's HiveContext. it’s possible to update data in Hive using ORC format. With transactional tables in Hive together with insert, update, delete, it does the "concatenate " for you automatically in regularly intervals. Currently this works only with tables in orc.format stored as orc Alternatively, use Hbase with Phoenix as the SQL. At this point, we have installed PySpark and created a Spark and SQL Context. Now to the important bit, reading and converting ORC data! Let’s say we have our data stored in the same folder as our python script, and it’s called ‘objectHolder’. To read it into a PySpark dataframe, we simply run the following.
My question has two parts: how can I set fine tune advanced ORC parameters using spark? Various posts show that there might be issues Spark Small ORC Stripes, How to set ORC stripe size in Spar. I am working with Spark SQL to query Hive Managed Table in Orc Format I have my data organized by partitions and asked to set indexes for each 50,000 Rows by setting 'orc.row.index.stride'='50000' lets say -> after evaluating partition there are around 50 files in which data is organized. Alert: Welcome to the Unified Cloudera Community. Former HCC members be sure to read and learn how to activate your account here.
Specifying as orc at the end of the SQL statement below ensures that the Hive table is stored in the ORC format. spark.sql"CREATE TABLE yahoo_orc_table date STRING, open_price FLOAT, high_price FLOAT, low_price FLOAT, close_price FLOAT, volume INT, adj_price FLOAT stored as orc" Loading the File and Creating a RDD. Apache Spark 2.3, released on February 2018, is the fourth release in 2.x line and has a lot of new improvements. One of the notable improvements is ORC suppor. One of TEXT, CSV, JSON, JDBC, PARQUET, ORC, HIVE, DELTA, and LIBSVM, or a fully-qualified class name of a custom implementation of org.apache.spark.sql.sources.DataSourceRegister. Important Since Databricks Runtime 3.0, HIVE is supported to create a Hive SerDe table.
--建表： create table tab_test name string, age int, num1 double, num2 bigint, msg varchar 80 --最后一个字段后面不能有 ',' 号 partitioned by p_age int,p_name string --分区信息 row format delimited fields terminated by ', '--数据中，属性间用逗号分隔 stored as textfile location ' /tab/test/tab_test '; --保存. import org. apache. spark. sql. hive. HiveUtils: import org. apache. spark. sql. sources. _ import org. apache. spark. sql. types. _ / Helper object for building ORC `SearchArgument`s, which are used for ORC predicate push-down.Due to limitation of ORC `SearchArgument` builder, we had to end up with a pretty weird double 从Spark2.3开始，Spark支持带有ORC文件的新ORC文件格式的矢量化ORC读取数据。为此，新添加了以下配置。当spark.sql.orc.impl设置为native并且spark.sql.o. 博文 来自： 杨鑫newlife的专栏. Recently I have compared Parquet vs ORC vs Hive to import 2 tables from a postgres db my previous post, now I want to update periodically my tables, using spark. Yes I know I can use Sqoop, but I prefer Spark to get a fine control.
Writing a Spark DataFrame to ORC files. Created Mon, Dec 12, 2016 Last modified Mon, Dec 12,. Alternatively, if you want to handle the table creation entirely within Spark with the data stored as ORC, just register a Spark SQL temp table and run some HQL to create the table. df.registerTempTable. A community forum to discuss working with Databricks Cloud and Spark. Create. Ask a. spark sql·java·orc. 9 Posts. 8 Users. 0 Followers. Topic Experts. sai krishna Pujari. 0 Points. Related Topics. parquet apache spark spark sql databricks java dataframe spark-sql spark 2.1.1 hivecontext pyspark snappy hive dataframes spark emr s3. Product. The system thinks t2 is an Acid table but the files on disk don’t follow the convention acid system would expect. Perhaps Xuefu Zhang would know more on Spark/Aicd integration.
Until Spark 2.4, the default ORC implementation remains Hive to maintain compatibility on old data. Spark is also able to convert existing datasource tables to use the vecorized reader, just set spark.sql.hive.convertMetastoreOrc=true default = false, native reader required. Finally the reader supports Schema evolution. Use the following steps to access ORC files from Apache Spark. To start using ORC, you can define a SparkSession instance: import org.apache.spark.sql.SparkSession val spark = SparkSession.builder.getOrCreate import spark.implicits._ The following example uses data structures to demonstrate working with complex types. However, using Spark SQL it takes about 5 minutes. Based on everything I see it sure seems like Spark is sweeping through the entire table. I've even set spark.sql.orc.filterPushdown=true, but it doesn't help. Is it reasonable to expect that Spark SQL's performance should be close to that of Hive's? I'm running HDP 184.108.40.206 using Spark 2.1.0.
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