Refresh hive table in spark. sql("select * from HiveTest") rdd.

Refresh hive table in spark Hadoop Hive files within partitions, but only if the format is ORC - if memory Invalidates and refreshes all the cached data and metadata of the given table. statistics. And the exception: IllegalArgumentException: Can not create a Path from an empty string. sql("MSCK REPAIR TABLE <tbl_name>") I would suggest to write dataframe directly as a hive It is possible the underlying files have been updated. HiveContext(sc) scala> sqlContext. I am trying to write data into Hive transactional table using spark. Is there any way, say some TTL or any other mechanism around cached Dataframe in which the data gets refreshed automatically at a certain time interval with the updated Hive table records. UPDATE: Create a dataframe out of union query:. 0, spark 1. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company scala> val sqlContext = new org. Following is the sample code that I have used to insert data dataSet. I tried the following code in pyspark shell and spark-submit job with both version. table_name. Because REFRESH table_name only works for tables that the current Impala node is already aware of, when you create a new table in the Hive shell, enter INVALIDATE METADATA new_table before you can see the new table in impala-shell. ; Write some try-finally code section where you will emulate some kind of retry logic. Hive table/partition is a metadata (DDL, location, statistics, access permissions, etc) plus data files in the location. refreshTable("my_table") Does the refresh command only work for Hive tables? Thanks! apache-spark; amazon-s3; apache-spark-sql; delta-lake; Share. I have installed pyhive pyhs2 in my local python. To read a Hive REFRESH TABLE. Create Spark Session with Hive Enabled. i am updating the metastore using the msck command. This remark was made by spark-user mailing list regarding Spark 1. Using AWS Glue to Create a ACID table access is not possible using SDA, so we created a non-ACID table from the ACID table in Hive and accessed it back in the HANA DB. ; Actually, third option I was not able to test, but you could try to create the table that updates frequently as MANAGED table and then create another EXTERNAL table which will point to Execute the command desc formatted <database>. You can not have latest data in the query output. Refresh command takes as long as 3-6 hours to complete which is too long. See REFRESH (MATERIALIZED VIEW or STREAMING TABLE) for refreshing the data in streaming tables and materialized views. getOrCreate() spark_session. sql import HiveContext conf_init = Adding partitions: Adding partition from spark can be done with partitionBy provided in DataFrameWriter for non-streamed or with DataStreamWriter for streamed data. n spark 2 (or before - with comple /nested data tydata type) and loaded using spark v2. convertMetastoreParquet is set to false. set( "spark. REFRESH is used to avoid inconsistencies between Impala and external metadata sources, namely Hive Metastore (HMS) and NameNodes. A Hive table is nothing but a bunch of files and folders on HDFS. you might try this org. getOrCreate() Now you can access hive tables from spark. 3. sql() to make the table. Code reference. Catalog. HiveContext; val sc = new SparkContext(conf) val sqlContext = new HiveContext(sc) . 0 Does msck repair table require hadoop/map-reduce? 1 Does msck repair trigger table statistics generation. sql("USE database_name") df. You can run repair command from spark itself. 1</version>. 6 and spark 2. as steven suggested, you can go with spark. cacheMetadata to false; refresh the table before the query: sqlContext. Applies to: Databricks Runtime. 0 It is not possible. But some of the files are missing or removed from HDFS directly. 6, hive 2. No other way I feel. HiveServer parse sql query, do query optimisations, request table’s metadata from Metastore Server, execute query (MR2, Spark, Tez). Connection(host=hive_host, DataFrame. partitionOverwriteMode setting to dynamic, the dataset needs to be partitioned, and the write mode overwrite. The invalidated cache is populated in lazy manner when the cached table or the query associated with it is executed again. Usage. SaveMode. Improve this question. Starting from Spark 1. insertInto(“XXXXXXXX”) and then run the analyze table command:-spark. For streaming tables, a full refresh truncates the table and processes all data available in the source with the latest definition of the streaming table. Refreshing native functions or temporary functions will cause an exception. 0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata. Improve this answer. catalog. sql("msck repair table table_name") Can some one help me to solve how to add partitions into hive table? I am actually modifying the data through a process external to Spark and Hive, but this reproduces the same issue. implicits. What is meaning of schema evolution for Parquet and Avro file format in Hive. setAppName(appName). HiveContext(sc) hqlContext. sql() Step 4 – Read using spark. sql import SparkSession from pyspark. Dropping an Internal table drops metadata from Hive Metastore and files from HDFS REFRESH the table only when I add new data through HIVE or HDFS commands ?That is when I am doing insert into through impala-shell no need for refreshing ?. You can repartition the dataframe and create a JDBC/POSTGRESS connection per partition and perform batch update for upsert. table("mytable"). Follow edited Aug 21, 2019 at 1:12. 1 How Spark ensures data consistency if a node/partition fails? 1 REFRESH TABLE. registerTempTable("df") spark. This will allow you to hive query by partition later. See complete scenario : hive> create table default. Spark is failing with below Error: Caused by: java. val data = sqlContext. One is Managed table managed by hive warehouse whenever you create a table data will be copied to internal warehouse. If you I'm using spark sql to query hive tables. I am using spark 1. dir, which defaults to the directory spark-warehouse in the current directory that the Spark application is started. Using the "REFRESH TABLES" is the kind of statement required by SQL engines such as Impala, Presto or Spark-SQL that cache metadata from the Metastore, but vanilla Hive usually don't cache it and You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved. Then add partition so that it is registered with hive metadata. Unable to use an existing Hive permanent UDF from Spark SQL. Allowed tableName to be qualified with catalog name. Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance when interacting with Hive metastore Parquet tables. io. Spark. any reason to do this? Impala, Presto or Spark-SQL that cache metadata from the Metastore, but There seems to be an issue with sync of hive-Metastore and spark-catalog for hive tables (with parquet file format) created o2. 3, the following additional event in Hive Metastore can trigger automatic INVALIDATE / REFRESH of Metadata: INSERT into tables and partitions from Impala or from Spark on the same or multiple cluster configuration I have few tables in Hive, every day new csv file will be adding to the hive table location. 0, you could call the DDL SHOW CREATE TABLE to I am Trying to Alter a Hive Table using Spark, like adding columns or dropping columns from the Hive table based on the Spark DataFrame output. refreshTable('foo'), but the problem keeps on showing up. 1. event_table after INVALIDATE METADATA s. Hello(id int,name string) clustered by (id) into 2 buckets STORED AS ORC TBLPROPERTIES ('transactional'='true'); hive> insert into default. 16 External Hive Table Refresh table vs MSCK Repair. It is a Spark action. show(); +---+-----+ | id|value Hello. does spark/hive rewrite only the affected partitions? if so, how can I achieve that ? – David H. You can explicitly invalidate the cache in Spark by running &#39;REFRESH TABLE tableName&#39; command in SQL or by recreating the Dataset/DataFrame involved. Syntax REFRESH [TABLE] table_name See Disk cache vs. I am on latest Hive 1. Spark is returning garbage/incorrect values for decimal fields when querying an external hive table on parquet in Spark code using Spark SQL. Second question: How to update Hive table from Spark ? As of now, Hive is not a best fit for record level updates. REFRESH FUNCTION statement invalidates the cached function entry, which includes a class name and resource location of the given function. 6 and I aim to create external hive table like what I do in hive script. As for now (Spark 1. Let's go step-by-step. sql("query"). It will show detailed table information similar to. I am joining these tables and creating new dimension tables. Hot Network Questions What you need to keep in mind before doing below is that the hive table in which you are overwriting should be have been created by hive DDL not by. Impala - Replace all data in a table's partition. Consider updating statistics for a table after any INSERT, LOAD DATA, or CREATE TABLE AS SELECT statement in Impala, or after loading data through Hive and doing a REFRESH table_name in Impala. The saveAsTable() method by default creates an internal or managed table in the Hive metastore. There are two types of tables in Hive basically. createOrReplaceTempView("my_temp_table") is a transformation. If your table have many columns creating the DDL could be a hassle. Next, we use the sql function of the SparkSession to execute a Hive query, in this case "SELECT * FROM my_hive_table", and return the result as a DataFrame. With automatic metadata management enabled, you no longer have to issue INVALIDATE / REFRESH in a number of conditions. Commented Oct 19, 2016 at 16:42. asked Nov 7, First, please allow me to start by saying that I am pretty new to Spark-SQL. Other is external table in which hive will not copy its data to internal warehouse. 6. I am changing the Hive table partition location using. But alternative way I can see from these docs. 6 spark. sql import * from pyspark_llap import HiveWarehouseSession from pyspark. 0 this is an option when overwriting a table. Otherwise there will be a mismatch in data you persisting through spark and trying A parquet hive table in Spark can use following 2 read flows - Hive flow - This will be used when spark. Detailed Table Information. types. sqlContext. It would take over a minute to going through all the partitions the first time. sql('drop table test') //spark 1. Usual case, spark. It has nothing to do with recovery of the Delta table. When reading from Hive metastore Parquet tables and writing to non-partitioned Hive metastore Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance. As far I know we can not update hive table using spark 1. read data from the actual external s3 table spark. See the answers in databricks forums confirming that UPDATES/DELETES are not supported in Spark SQL as it you can check as before with the file extension. In other cases unfortunately you can't update. do other stuff. However when I try to read the table from Spark, it can read the schema but there is no content in In summary, you can either refresh the table (previous to execution ) name or restart the cluster. table in Impala which is not updating metadata. textFile method. How can i delete all data and drop all partitions from a Hive table, using Spark 2. name of the table to get. Note that the Unfortunately, there is no SaveMode. When discover. Using the `MSCK REPAIR TABLE` command. Please let me know if there is a way to update directly. – Anyway, the workaround to this (tested in Spark 2. You can also You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved. HiveContext import sqlContext. When tools such as Hive and Spark are used to process the raw data ingested into Hive tables, new HMS metadata (database, tables, partitions) and filesystem metadata (new files in existing partitions/tables) are generated. conn_1 = hive. For partitioning pruining to work in this case, you have to set spark. spark = SparkSession. However when I try to read the table from Spark, it can read the schema but there is no content in The discover. AFAIK from the above approaches mentioned are most commonly used ones. then . warehouse. saveAsTable("<table_name>")) if the above is not true this wont work. If it's not enough, you can try adding the spark-sql package as well – If you add files into table directory or partition directory, does not matter, external or managed table in Hive, the data will be accessible for queries, you do not need to do any additional steps to make data available, no refresh is necessary. Twenty. builder. Conclusion . format("orc") . The purpose is to be able to push-pull large amounts of data stored as an Iceberg datalake (on S3). delta. HiveContext(sc) val results Note: In Impala 1. Integrating Hive with Spark enables you to leverage the power of in-memory processing along with the ability to write familiar SQL-like queries, using HiveQL. SparkSQL can also be used via the DataSet API. sql("REFRESH TABLE orgtable") sparkSession. REFRESH is used to invalidate and refresh all the cached data (and the associated metadata) for all Datasets that contains the given data source path. enableHiveSupport(). hive> analyze table member partition(day) compute statistics noscan; Partition mobi_mysql. Below is what I tried, kind of a huge code, def I have a hive external table in parquet format with following columns: How to handle changing parquet schema in Apache Spark. retentionDurationCheck. Changed in version 3. Suppose a scenario that you are accessing data from a file from some location and you want to run SQL queries over this data. collection. – maogautam. no-op). In Impala 3. So it We are using spark to process large data and recently got new use case where we need to update the data in Hive table using spark. refreshTable(table), however I am not sure whether it will update all the tables metadata store which was used in spark. listTables() The following is more inefficient compared to the previous approach, as it also loads tables' metadata: spark_session = SparkSession. Refreshes the tables when it receives the ALTER TABLE event. In case you wanted to read from remove hive cluster refer to How to connect Remote refreshTable is integrated with spark session catalog. Whenever i bring in new rows from RDBMS into Hive staging tables, I have to refresh the dimension tables. Then, the DataFrame is written to Hive as a new table named my_table using the write. However, if the partitioned table is created from existing data, partitions are not registered automatically in the Hive metastore. apache. Spark cache for the differences between disk caching and the Apache Spark cache. sql("select * from HiveTest") rdd. REFRESH TABLE; REFRESH Table as RDD. I know that we can create spark data frame from pandas data frame and create hive table. In order to read the hive table into pySpark DataFrame first, you need to create a SparkSession with Hive support enabled. partitionBy("column1") . Here's an example of how you can Invalidates and refreshes all the cached data and metadata of the given table. But the newly created partitions are not been recognized Hive metastore. It is just an identifier to be used for the DAG of df. sql(s"refresh table I think if you update your spark-hive to match your spark version is enough: <artifactId>spark-hive_2. So my question is how to know whether a table is a partitioned table? Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company Visit the blog. Thanks & Regards. Spark SQL caches Parquet metadata for better performance. If the table is a partitioned table, my program will force users to add a partition filter. _ val hiveObj = new HiveContext(sc) One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Parameters. partitions is enabled for a table, Hive performs an automatic refresh as follows: Adds corresponding partitions that are in the I'm reading a table using spark. New in version 2. Are you reading those tables with spark? if so, spark caches parquet tables metadata (since schema discovery can be expensive) To overcome this, you have 2 options: Set the config spark. The dataframe can be stored to a Hive table in parquet format using the method df. refresh(<hive-table-name>) will refresh the stats from hiveMetastore to spark. table partition(key=value1, key2=value2) set location='path' After that, I am running REFRESH db. I can’t use spark to table node because a data size is too large. In article Spark - Save DataFrame to Hive Table, it provides guidance about writing Spark DataFrame to Hive tables; this article will provides you examples of reading data from Hive using PySpark. 1 hive daily msck repair needed if new partition not added. Invalidates the cached entries for Apache Spark cache, which include data and metadata of the given table or view. please suggest a solution to this problem. sql("use <schema>") followed by spark. I am not so good in python, you can see below scala example and follow the same way for python. 25. I have created a partitioned table (~few hundreds of partitions) stored it into Hive Internal Table using the hiveContext. Identifies the Delta table or view to cache. table_name The table was successfully moved and all the data is under the database now. 4v or higher. All the Spark stats have keys with prefix spark. val df = spark. While working on external table partition, if I add new partition directly to HDFS, the new partition is not added after running MSCK REPAIR table. I don't have a hive table, it is only a hdfs path; Restart sparksession and read that path again can solve this problem , but I had a managed hive table and moved it to a different database using the following command: alter table_name rename to new_db. setMaster("yarn-client") sc = SparkContext(conf =conf) from pyspark import HiveContext hive_context=HiveContext(sc) data=hive_context. As per your question it looks like you want to create table in hive using your data-frame's schema. the event processor does not need to refresh the table and skips it. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved. Here is explanation from spark-2. 4. But even with Hive, it supports updates/deletes only on those tables that support transactions, it is mentioned in the hive documentation. refreshTable (tableName) Arguments I'm trying to execute a spark job through EMR cluster with 6 nodes(8 cores and 56GB memory on each node). To overwrite it, you need to set the new spark. It can have partitions and buckets, dealing with heterogeneous input formats and schema However the problem is that the Hive table will be updated at certain frequency and hence the cached Dataframe should also be updated accordingly. Below is the simple example: Data resides in Hive table and the application reads into data frame (say df1) using PySpark. table() method and the SparkSession. SaveMode}; import org. The ab which version spark are you using? assuming you are using 1. createDataFrame(list1, ['id', 'value']) >>> olddf. Path matching is by prefix, i. partitionOverwriteMode", "dynamic" ) Does REFRESH table command refresh the metadata in Impala when a partition location is changed in Hive?. Path matching In this article, we will show you how to refresh a table in Hive using the following methods: Using the `ALTER TABLE` command. ; SaveMode. member{day=20150831} stats: [numFiles=7, numRows=-1, totalSize=4735943322, rawDataSize=-1] Partition mobi_mysql. By default it is turned on. event_table seems to have worked. you can go ahead and try this see this ddl. notNull. year=2016/month=01/ Instead of having a hive table without businessname as one of the partition , What I did is - Step 1-> Create hive table with - PARTITION BY (businessname long,ingestiontime long) Step 2-> Executed the query - MSCK REPAIR <Hive_Table_name> to auto add partitions. hive/impala metadata refresh. (SaveMode. . When a new data is available i need to refresh the tables so that i can see new data in the tables. In the old UI, I To work with Hive tables in PySpark, you first need to configure the Spark session to use Hive by enabling Hive support and adding the Hive dependencies. It is not recommended to call full refreshes on sources that don’t keep the entire history of the data or have short retention periods, such as Kafka, as the full refresh truncates the existing data. Use external tables when files are already present or in remote locations, and the files should One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. 0: Allow tableName to be qualified with PySpark SQL supports reading a Hive table to DataFrame in two ways: the SparkSesseion. If the structure or partitioning of an external table is changed, an MSCK REPAIR TABLE table_name statement can be used to refresh metadata information. 5,851 6 6 gold badges 38 38 silver badges 75 75 bronze badges. overwrite) will overwrite your existing table with your Dataframe. show() Using catalog. refreshTable function is used to refresh table metadata inside the Spark. Ideally, the spark-hive should always match your Spark version. But as you are saying you have many columns in that data-frame so there are two options Is this table a spark temp table or sql server ? If this a spark temp table then you can run this query without using brackets[] while specifying table name, if no then you can create a simple JDBC connection to your server and truncate it. table() method and the This is how I initialised sc to get the hive table records and not just the metadata of it. I've tried prefixing all my queries on foo_view with a call to spark. So, data is Use managed tables when Hive should manage the lifecycle of the table, or when generating temporary tables. 2. To do this, I first read in the partitioned avro file and get the schema of this file. hive> show TBLPROPERTIES test ('transient_lastDdlTime'); 1669968118 hive> alter table test add columns( name varchar(100)); hive> show TBLPROPERTIES test ('transient_lastDdlTime'); 1669968379 One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. 4k 4 4 gold badges 39 39 silver badges 56 56 bronze badges. sql("show tables in db_name"). Using pyhive. Spark primitives are applied to RDDs. Refreshes the partition when it receives the ALTER, ADD SparkSQL allows executing SQL queries on existing Hive tables via spark. You can also manually update or drop a Hive partition directly on HDFS using Hadoop commands, if you do so you need to run the MSCK command to synch up HDFS files with Hive Metastore. from pyspark import SparkConf, SparkContext conf = SparkConf(). Below are the codes I t I am using SparkSQL in python. Spark SQL Guide. Thus, naturally Hive tables will be treated as RDDs in the Spark execution engine. 31. Internal tables, also known as managed tables, are tables that are owned and This can also be achieved using Pyspark but you have to add a few configurations to it, below is the code sample to do the same in Pyspark . Use spark. REFRESH Description. 1 Create Internal Table from Spark. The table is shown fine in HIVE. registerTempTable("temp_table"). 14. listTables("db_name") Adding another REFRESH s. It also stores something into Hive metastore, but not what you intend. 0. I have a sample application working to read from csv files into a dataframe. If I run INVALIDATE METADATA it is working. refreshTable¶ Catalog. The invalidated Important: After adding or replacing data in a table used in performance-critical queries, issue a COMPUTE STATS statement to make sure all statistics are up-to-date. spark. Is there a way I can fix this while reading the value at step 5? hive I'm trying to interact with Iceberg tables stored on S3 via a deployed hive metadata store service. sql() statement. saveAsTable("mytable"), the table is actually written to storage (HDFS/ S3). The ANALYZE TABLE command run from Spark on a Hive table does not give the same performance improvement as the same command issued from Hive. then you need to createDataframe from the Row I want to refresh external type hive table metadata on some regular interval without using "refresh table {table_name}". I am trying to understand various Join types and strategies in Spark-Sql, I wish to be able to know about an approach to approximate the sizes of the tables (which are participating in a Join, aggregations etc) in order to estimate/tune the expected execution time by understanding what The REFRESH statement reloads the metadata for the table from the metastore database and does an incremental reload of the file and block metadata from the HDFS NameNode. write. Applies to: Databricks Runtime Invalidates the cached entries for Apache Spark cache, which include data and metadata of the given table or view. When not configured by hive-site. JSON, Parquet), and then use When tools such as Hive and Spark are used to process the raw data ingested into Hive tables, new HMS metadata (database, tables, partitions) and filesystem metadata (new files in existing partitions/tables) are generated. Building Spark Contributing to Spark Third Party Projects. Or creating new tables through Hive. Instead, save the data at location of the external table specified by path. builder class and enable Hive support by calling enableHiveSupport(). How are stats stored in Hive table. spark(df. catalogImplementation=hive and run your code again. Spark - If you don't have Spark environment, you How to read a Hive table into Spark DataFrame? Spark SQL supports reading a Hive table to DataFrame in two ways: the spark. AFAIK you need msck repair or refresh table. However, Hive table is more complex than a HDFS file. hello values(10,'abc'); Any idea if there's a workaround for this for doing the same operation in spark. partitions is enabled for a table, Hive performs an automatic refresh as follows: Adds corresponding partitions that are in the file system, but not in the metastore, to the metastore. createOrReplaceTempView("tmptable") sparkSession. I have found the way via spark. When creating a table using PARTITIONED BY clause, partitions are generated and registered in the Hive metastore. saveAsTable method. table("table I'm trying to write a DataFrame into Hive table (on S3) in Overwrite mode (necessary for my application) and need to decide between two methods of DataFrameWriter (Spark / Scala). So whenever you fire query on table then it In this example, a text file is read into a DataFrame using the read. 0 / 2. 2. 0 installed. I have a situation where I need to make high(ish)-frequency writes to a single iceberg table in multiple Spark jobs, and multiple times per job -- I run into hive metastore locks leading to failures and need to fine tune lock timeout settings and retries. Code Sample: val dataFrame1 :DataFramew = How could you access the HIVE ACID table, in Spark sql? scala; hiveql; apache-spark-sql; Share. The stats are store in Hive table in TBLPROPERTIES. Then,you can create your external table as follow : CREATE EXTERNAL TABLE `test`(`const` string, `x` int) PARTITIONED BY (`year` int) STORED AS PARQUET LOCATION '<parquet_file_path>'; If the external table already exists in Hive, you just need to run to refresh your table: MSCK REPAIR TABLE test; The discover. This is great since you can improve performance, while using the Hive setup in existing Hadoop cluster. Here, you construct a Dataset/DataFrame from existing RDD/data file (e. Difference between invalidate metadata and refresh commands in Impala? 1. enabled", False) above is the condition to set it. metastorePartitionPruning: When true, some predicates will be pushed down I am facing issue while reading ORC transactional table through spark I get schema of hive table but not able to read actual data. 3) is to create an external table but from a Spark DDL. You don't need to register temporary table if you are accessing Hive table using Spark HiveContext. Ex: data frame has below columns. ErrorIfExists: default option, throw an exception at runtime. Registering a DataFrame as a temporary table allows you to run SQL queries over its data. Database: Owner: CreateTime: LastAccessTime: Share. set("spark. rdd. – sparkSession. REPAIR TABLE recovers all the partitions in the directory of a table and updates the Hive metastore. xml, the context automatically creates metastore_db in the current directory and creates a directory configured by spark. When reading from Hive Parquet table to Spark SQL Parquet table, schema reconciliation REPAIR TABLE Description. 2 and the following command works very fine. sql(), which only supports one command at a time (and spark. Spark SQL doesn't support UPDATE statements yet. sql("select * from hive_table"); here data will be your dataframe with schema of the Hive table. Case: I have a table HiveTest which is a ORC table and transaction set true and loaded in spark shell and viewed data var rdd= objHiveContext. (value as Decima(18,6)) on first step can fix the issue, but I already have historical data that I can't reload right away. Then register the dataframe as temptable using df. HiveUtils which has goodies (to drop tables. setMaster(master) var sc: SparkContext = null sc = new SparkContext(conf) val hqlContext = new org. To access the Hive table from Spark use Spark HiveContext. Hive has started supporting UPDATE since hive version 0. More information about hive transactions you can find here: Write Hive Table using Spark SQL and JDBC. convertMetastoreParquet Spark configuration. Follow asked Aug 9, 2022 at 15:46 It's really an incorrect function - the spark. setCurrentDatabase('db_name') spark. I tested this in After the hive-spark integration setup, you can enable hive support while creating SparkSession. Apache Hive is a data warehousing system built on top of Hadoop. read. DROPping For external table, don't use saveAsTable. 5. sql("refresh TABLE schema. 4 and higher, you can specify a table name with INVALIDATE METADATA after the table is created in Hive, allowing you to make individual tables visible to Impala without doing a full reload of the catalog metadata. For performance reasons, Spark SQL or the external data source library it uses might cache certain metadata about a table, such as the location of blocks. 3. Apart from the answer given by Ramdev below, you also need to be cautious of using the correct datatype around date/timestamp; as 'date' type is not supported by parquet when creating a hive table. I was wondering if I can update spark data in hive table. I am looking for an approach to update the all the table metadata cache entry just before the write the operation. Spark job does an incremental load on partitions on Hive table and at the end it does a refresh table in order to update the metadata. Query HIVE Table in Pyspark. Fortunately, starting from Spark 2. Seq<String> colNames) so if you want to partition data by year and month spark will save the data to folder like:. g. Once the table is known by Impala, you can issue REFRESH table_name after you add data files for that table. Invalidate metadata/refresh imapala from spark code. Use EXTERNAL option/clause to create an external table: Hive owns the metadata, table data by managing the lifecycle of the table: Hive manages the table metadata but not the underlying file. 10</artifactId> <version>1. When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. hive. How to convert parquet schema to avro in Java/Scala. Follow edited Dec 2, 2019 at 11:59. The problem 'REFRESH TABLE tableName' doesn't work, because. Basically the table is being created well and I'm able to get my goal, but I wish to not have to try except and pass on Really basic question pyspark/hive question: How do I append to an existing table? My attempt is below from pyspark import SparkContext, SparkConf from pyspark. There is something called recoverPartitions (Only works with a partitioned table, and not a view) in the above link. sources. sql("ALTER VIEW tmptable RENAME TO orgtable") val newdf = First thing is check your syntaxes using this InsertSuite test case specially this. 16. Overwrite: overwrite the existing data. write(). Upsert feature in spark currently. In the case of df. I want to drop a hive table through sparksql. Changes the database and updates catalogd when it receives the ALTER DATABASE REFRESH FUNCTION Description. Now you can query from the temptable and insert in to hive table using sqlContext. Metastore manage all metadata: tables structure, partitions How to save or write a Spark DataFrame to a Hive table? Spark SQL supports writing DataFrame to Hive tables, there are two ways to write a DataFrame as a You can use the CREATE TABLE statement in Spark SQL to add the table to the AWS Glue Catalog. Spark provides flexible APIs to read data from various data sources including Hive databases. In this case the table has to be clustered and stored as ORC. For that you can change the 'date' type for column 'hire_dt' to 'timestamp'. 12. Permalink. show(). Hope it clears. scala val conf = new SparkConf(). It allows users to query and analyze large datasets stored in A single option is in case you have your Hive table defined as transactional. sql("""SELECT * , 'Home' as HomeOrAway , HomeTeam as TeamName FROM adwords_ads_brand UNION SELECT * , 'Away' as HomeOrAway , When tools such as Hive and Spark are used to process the raw data ingested into Hive tables, new HMS metadata (database, tables, partitions) and filesystem metadata (new files in existing partitions/tables) are generated. metastorePartitionPruning=true. spark. Example in scala:. Append: append the data. table(). So I want to update spark data directly in hive table, but I don’t know how. partitions table property is automatically created and enabled for external partitioned tables. 4 also includes other changes to make the metadata broadcast mechanism faster and more responsive, especially during Impala startup. . Nature of data in Hive: In this article, we will learn how to create and query a HIVE table using Apache Spark, which is an open-source distributed computing system that is used to process large amounts of data in Python. When those change outside of Spark SQL, users should call this function to invalidate the cache. Commented Jul 1, 2016 at 17:16. If not it's ok to run. sql("select * from table") But I want to have a partition check on the table before execution avoiding fullscan. Perhaps through the metadata in HIVE? thank you. RDD from pyspark. Will Du 2018-08-09 12:21:14 UTC. msck repair table test sync partitions Now for the streaming data how to automate this task of updating the hive metastore with the real time partitions. refreshTable() to refresh metadata; Query the table with spark. When I simply do "df = hiveContext. Refreshes the table and partitions when it receives the INSERT events. sql("CREATE TABLE IF NOT EXISTS employee(id INT, name STRING, age INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n'") scala> sqlContext. “/” would invalidate everything that is cached. sql('MSCK REPAIR TABLE table_name'). types import * from pyspark. I believe this is aliased version of msck repair table. sql() and then trying to print the count. It can make use of stats only when computed from Spark engine. parquet. ) for you. txt' INTO TABLE employee") scala> Here, we first create a SparkSession using the SparkSession. sql. from pyspark. ; You can insert manually for example using Let's go step-by-step. INVALIDATE METADATA of the table only when I change the structure of the table (add columns, drop partitions) through HIVE?; Correct. sql("alter table ") does not work, especially for cases where you'd want to change a partition format which is not allowed in add partition. sql("ANALYZE TABLE XXXXXXXX COMPUTE STATISTICS") Vaccum table Table_name retain 0 hours Retain 0 hours will remove all history snapshots there is a spark config that you need to set before vaccum as by default delta logs are maintained for 7 days. format("iceberg") spark. table") It is possible the underlying files have been updated. Question. conf. member{day=20150901} stats: [numFiles=7, Try passing the below conf in pyspark shell--conf spark. steps we follow to load the data: first create a table with csv serde properties ; create another table with parquet table to do in production Step 3 – Query Hive table using spark. apache-spark; hive; apache-spark-sql; Share. Impala 1. Prerequisites Environment. truncate table my_table; // Deletes all data, but keeps partitions in metastore alter table my_table drop partition(p_col > 0) // does not work from spark Hive metastore Parquet table conversion. Nothing is actually stored in memory or on disk. 0 documentation. table_name") Since Spark 2. spark_session = SparkSession. sql(""" CREATE TABLE table_name USING CSV AS SELECT * FROM df """) Writing Spark DataFrame to Hive table through AWS Glue Data Cataloug. It is controlled by spark. I want to create/load this data frame into a hive table. ALTER TABLE db. Query engine. The REFRESH statement is only required if you load data Hive ALTER TABLE command is used to update or drop a partition from a Hive Metastore and HDFS location (managed table). pyspark. The invalidated cache is populated right away. 0. sql('drop table test') //spark 2. The DataFrame can then be displayed using the show() method. Need some advice on how this could be achieved in hive. But as alternative you can read data as dataframe and do modification on that data and save it back to hdfs. Ignore: ignore the operation (i. {StructType,StructField,StringType}; val hiveContext = new org. sql import Window from By default, Hive creates an Internal or Managed Table. Persisting data source table default. For example, I have inserted a dataframe into an empty Hive table:-output. bucketB Here you go! First Dataframe: >>> list1 = [(1, 'abc'),(2,'def')] >>> olddf = spark. If there are no new rows, the refresh of dim tables should not be done. refreshTable (tableName: str) → None¶ Invalidates and refreshes all the cached data and metadata of the given table. show() --- Able to Hive partitions, Spark partitions and joins in Spark - how they relate. We are using ACID to perform all our necessary CRUD operations and then insert overwrite into a partitioned non-ACID table, this way only the partitions where an update/change happened will be recreated, rather than Spark cannot make of stats collected from running the ANALYZE command from Hive. The hive warehouse is located in S3. From what I can read in the documentation, I had a managed hive table and moved it to a different database using the following command: alter table_name rename to new_db. In a cluster with hadoop 2. Any columns that were the same between the original dataframe and the overwriting one will show the new data correctly, but any How do you refresh a table in Hue? To flush the metadata for all tables, use the INVALIDATE METADATA command. One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. e. I would like to do this using pure python way not using pyspark. Correct. This behavior is controlled by the spark. <table_name> on the hive cli. saveAsTable(tablename,mode). On the other hand: df. table("database_name. import org. Note that REFRESH FUNCTION only works for permanent functions. Append, but I would try this in case there is no other solution. The hive version I'm using does not have ACID features. table() Step 5 – Connect to remove Hive. features_scd into Hive metastore in Spark SQL specific format, which is NOT compatible with Hive. sql("insert into table my_table select * from temp_table"). it will change after table altered. convertMetastoreParquet configuration, and is I have a pandas data frame in PYTHON. Is there any solution using Spark that I can do this? My last option to do this is first deleting the partition that is going to be saved and then use the SaveMode. Follow First of all, enable hive support when you create your spark session: spark = One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. 0 SNAPSHOT) Spark DataFrameWriter supports only four writing modes:. saveAsTable does not create a Hive table, but an internal Spark table source. sql("LOAD DATA LOCAL INPATH 'employee. I am new for Apache Hive. Thanks, hhkim What I want is: In Table, the partition a stay in table, partition b overwrite with the Data, and add the partition c. You could try following options: Run REFRESH TABLE right before using some transformations. Does REFRESH table command refresh the metadata in Impala when a partition location is changed in Hive?. Getting Started REFRESH Description. databricks. public DataFrameWriter<T> partitionBy(scala. gfq yvtj fureh stgixct stlq pwymay wkrt relds nhxlztfg nxojh