I want to create a Hive table/view which will be accessing BigSQL (BigInsights 4.2) table. Data will be loaded in BigSQL table and I'm trying to fetch that data from Hive. Is there any procedure to sync data from BigSQL and Hive table?
It will be automatic. the tables already belong to Hive, so when you insert data in a bigSQL Hadoop table you should be able to see it through Hive queries.
The procedure that synchronizes Hive MetaStore and BigSQL is HCAT SYNC , it runs automatically.
db2 "call SYSHADOOP.HCAT_SYNC_OBJECTS('Schema', 'TableName', 'a', 'REPLACE', 'CONTINUE')"
As may be IBM Knowledge Center :
Tables that are created under the Hive default schema are not automatically synced; you must synchronize these tables manually if you want them in Db2 Big SQL.
You can invoke the HCAT_SYNC_OBJECTS stored procedure for all current schemas and tables by selecting the Run Metadata Sync service actions menu item.
Related
I have a requirement where I might have to update the Bigquery External tables on a periodic basis.
The GCS location has timestamp for every incremental run, I would like to update to the latest timestamp folder as the path of External table.
One way i see is only dropping the table and creating again by pointing it to latest folder. But, is there any other way to update it without dropping the table
As suggested by #Samuel , you can use the SQL statement CREATE or REPLACE EXTERNAL TABLES for your requirement. Scheduled queries support DML and DDL statements which can be used to create the new tables. You can use the below mentioned query parameter to create the table according to your schedule :
My_database_name.my_table_name.my_results_{run_date}
For more information you can refer to this documentation.
Are two hive tables (native, external) always required for querying a DynamoDB table from an AWS EMR?
I have created a native hive table (CTAS, create table as select) using an hive external table that was mapped to a DynamoDB table. My (read) query times against external tables are slow and it uses up the read throughput versus native table are fast and read throughput is not consumed.
My questions:
Is this a standard practice/best practice i.e., create an external table mapped to a dynamodb table and then create a CTAS and query against CTAS for all read query use cases?
Where or how GSI's on dynamodb come into picture on hive side of things? Toward this curiosity I have tried to map my external hive table column to dynamodb GSI and some what expectedly saw NULLs.
So, back to #2 question was wondering how are GSI's used with a native or external hive table?
Thanks,
Answer is no.
However, from my observation if a hive native table data is backed (CTAS) by hive external table that is referencing a DynamoDb table: Read data is not accounted if you are querying hive native table from EMR. If you to take into account the periodic update (refresh data) of hive native table.
When we delete a table from a Hive database, will information about this delete is captured in hive metastore tables?
Currently i am working on cataloging the hive metadata to Apache Atlas. i need to update Atlas whenever there is a table deleted in the Hive DB.
But in hive metastore tables not able to find information about table deletion in Hive DB. Hive actually deletes complete metadata of deleted hive table, does it not mark or capture this table deletion?
-
-
I'm using Azure HDInsights, Azure Data Lake and Hive via Ambari.
I'm setting up a test environment. The original environment's data is stored on Azure Data Lake, in the form of ORC files loaded via Hive. I copied all the data from the original Data Lake to the test Data Lake via Data Factory successfully.
When I try to create my Hive ORC tables in the test environment and then query them no records are returned. Schema/Folder locations on the respective data lakes are the same, am I missing something related to the metastore since it's a different one on test?
Edit: I want to add that I set up an external table to the Test environment's Data Lake in SQL Datawarehouse using Polybase and that is able to read the data just fine.
As chemikadze mentioned, running MSCK REPAIR TABLE <your-table> fixed it. My tables were partitioned and so the metastore didn't know to look in certain sub-folders for locating the data.
The following pattern now helps me accomplish an environment duplication:
Create Data Factory Pipeline to copy Data Lake folders from Dev -> Test.
Run Hive DDL on Test environment.
Run repair table command on each of the partitioned tables created in Test environment.
In a cluster having Hive installed, What does the metastore and namenode have? i understand that the Metastore has all the table schema and partition details and metadata. Now what is this metadata? then what does the namenode have? and where is this metastore present in a cluster?
The NameNode keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It also keeps track of all the DataNode(Dead+Live) through heartbeat mechanism. It also helps client for reads/writes by receiving their requests and redirecting them to the appropriate DataNode.
The metadata which metastore stores contains things like :
IDs of Database
IDs of Tables
IDs of Index
The time of creation of an Index
The time of creation of a Table
IDs of roles assigned to a particular user
InputFormat used for a Table
OutputFormat used for a Table etc etc.
Is this what you wanted to know?
And it is not mandatory to have metastore in the cluster itself. Any machine(inside or outside the cluster) having a JDBC-compliant database can be used for the metastore.
HTH
P.S : You might find the E/R diagram of metastore useful.
Hive data (not metadata) is spread across Hadoop HDFS DataNode servers. Typically, each block of data is stored on 3 different DataNodes. The NameNode keeps track of which DataNodes have which blocks of actual data.
For a Hive production environment, the metastore service should run in an isolated JVM. Hive processes can communicate with the metastore service using Thrift. The Hive metastore data is persisted in an ACID database such as Oracle DB or MySQL. You can use SQL to find out what is in the Hive metastore:
Here are the tables in the Hive metastore:
SQL> select table_name from user_tables;
DBS
DATABASE_PARAMS
SEQUENCE_TABLE
SERDES
TBLS
SDS
CDS
BUCKETING_COLS
TABLE_PARAMS
PARTITION_KEYS
SORT_COLS
SD_PARAMS
COLUMNS_V2
SERDE_PARAMS
You can describe the structure of each table:
SQL> describe partition_keys;
TBL_ID NUMBER
PKEY_COMMENT VARCHAR2(4000)
PKEY_NAME VARCHAR2(128)
PKEY_TYPE VARCHAR2(767)
INTEGER_IDX NUMBER(10)
And find the contents of each table:
SQL> select * from partition_keys;
So if in Hive you "CREATE TABLE xxx (...) PARTITIONED BY (...)" the Hive partitioning data is stored into the metastore (Oracle, MySQL...) database.
For example, in Hive if you create a table like this:
hive> create table employee_table (id bigint, name string) partitioned by (region string);
You will find this in the metastore:
SQL> select tbl_id,pkey_name from partition_keys;
TBL_ID PKEY_NAME
------ ---------
8 region
SQL> select tbl_name from tbls where tbl_id=8;
TBL_NAME
--------
employee_table
When you insert data into employee_table, the data will be stored in HDFS on Hadoop DataNodes and the NameNode will keep track of which DataNodes have the data.
Metastore - Its a database which stores metadata a.k.a all the details about the tables you create in HIVE. By default, HIVE comes with and uses Derby database. But you can use any other database like MySQL or Oracle.
Use of Metastore: Whenever you fire a query from your Hive CLI, the Execution engine gathers all the details regarding the table and creates an Execution plan(Job). These details comes from Metastore. Finally the Execution engine sends the Job to Hadoop. From here, the common Hadoop Map Reduce Job is executed and the result is send back to Hive. The Name node communicates with Execution engine to successfully execute the MR Job.
Above diagram is excellent one to understand Hive and hadoop communication.
Regarding Hive-Metastore (not hadoop - metastore):
It is not necessary/compulsory to have metastore in your hadoop environment as it is only required if you are using HIVE on top of your HDFS cluster.
Metastore is the metadata repository for HIVE only and used by HIVE to store created database object's meta information only(not actual data, which is already in HDFS because HIVE do not store data. Hive uses already stored datain File system)
Hive implementation required a metastore service using any RDBMS.
Regarding Namenode (hadoop -namenode):
core part of Hadoop, which behaves like metastore for cluster.
Not a RDBMS . Stores file system meta info in File System only.