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Ans: – It is a tool in the Hadoop echo system which is basically used to transfer the data between RDBMS and Hadoop. It can import data from RDBMS to Hadoop and export data from Hadoop to RDBMS.
Ans: Below is the lists of RDBMSs that are supported by Sqoop Currently.
1. MySQL
2. PostGreSQL
3. Oracle
4. Microsoft SQL
5. IBM’s Netezza
6. Teradata
Currently, Sqoop Supports data imported into the below services.
1. HDFS
2. Hive
3. HBase
4. HCatalog
5. Accumulo
In Sqoop Majorly Import and export commands are used. But below commands are also useful some times.
1. codegen
2. eval
3. import-all-tables
4. job
5. list-databases
6. list-tables
7. merge
8. metastore
4 mappers will run by default and no reducer.
We can control/increase/decrease the speed of copying by configuring the number of map tasks to be run for each scoop copying process.
We can do this by providing an argument -m 10 or –num-mappers 10 argument to sqoop import command.
If we specify -m 10 then it will submit 10 map tasks parallel at a time.
Based on our requirement we can increase/decrease this number to control the copy speed.
We need to use –direct argument in import command to use direct import fast path and this –direct can be used only with MySQL and PostGreSQL as of now.
It is used to specify the column of the table used to generate splits for imports.
This means that it specifies which column will be used to create the split while importing the data into your cluster.
It can be used to enhance the import performance by achieving greater parallelism.
Sqoop creates splits based on values in a particular column of the table which is specified by –split-by by the user through the import command.
If it is not available, the primary key of the input table is used to create the splits.
##.Reason to use: Sometimes the primary key doesn’t have an even distribution of values between the min and max values(which is used to create the splits if –split-by is not available).
In such a situation you can specify some other column which has proper.
The -P argument (prompts for user password) will read a password from a console prompt,
and is the preferred method of entering credentials.
We can use –incremental append argument to pull only the new rows from the RDBMS table into the HDFS directory.
In this case, we need to use –incremental lastmodified argument with two additional mandatory arguments –check-column
and –last-value (value).
The default file type is text file format. It is the same as specifying –as-textfile clause to sqoop import command.
It is a tool using which Sqoop hosts a shared metadata repository.
Multiple users and/or remote users can define and execute saved jobs (created with sqoop job) defined in this metastore
It allows the user to run sample SQL queries against the Database and preview the results on the console.
By using the WHERE clause in the sqoop import statement we can import only a subset of rows.
By using the –column parameter in which we mention the required column names as a comma-separated list of values.
sqoop import –connect com.jdbc.mysql://IP_Address/dbname –username XXXX –password XXXX –table table_name –create-hive-table
sqoop job –list
Use export
sqoop export –connect jdbc://ipaddress/databasename –username username –password password –table tablename –export-dir /dirname/part-m000000 -m 1
Use import-all-tables
sqoop import-all-tables –connect jdbc:mysql:ipaddress/databasename –username –P –target-dir /dirname -m 1
where clause use for filtering the data which is going to be import to HDFS.
sqoop import –connect jdbc://ipaddress/databasename –username username –password password –table tablename -target-dir /dirname -m 1 –where “id >= 2”
To incrementally append the data to the existing HDFS we use incremental clause in sqoop.
sqoop import –connect jdbc:mysql://mysql.example.com/sqoop –username root -P –table emp
–incremental append –check-column id –last-value 7 -m 1
We use incremental lastmodified for such type of mutable imports.
sqoop import –connect jdbc:mysql://mysql.example.com/sqoop –username root -P –table emp
–incremental lastmodified –check-column last_update_date –last-value “2019-01-29 23:52:11” -m 1
This command is use to create a job with a particular function and can be use again and again without writing the whole command.
sqoop job –create incremental_job — import -–connect jdbc:mysql://mysql.example.com/sqoop –username root -P –table emp
–incremental lastmodified –check-column last_update_date –last-value “2019-01-29 23:52:11” -m 1
sqoop job -exec job_name
we can use –hive-import:
sqoop import –connect jdbc:mysql://Ip_addrss/dbname –table EMPLOYEES –hive-table Emp
–hive-import
sqoop import –connect jdbc:mysql://Ip_addrss/dbname –table EMPLOYEES
–split-by dept_id -m 2
use –as-avrodatafile
sqoop import –connect jdbc:mysql://Ip_addrss/dbname –table EMPLOYEES
–as-avrodatafile –target-dir /avro -m 4
=> sqoop list-databases –connect jdbc:mysql://Ip_addrss –username user -P
sqoop list-tables –connect jdbc:mysql://Ip_addrss/db_name –username user -P
Using the –input-null-string parameter, a default value can be specific so that the row gets insert with
the default value for the column that it has a NULL value in HDFS.
sqoop import –connect jdbc:mysql://localhost:3306/sqoop –username sqoop -P –table cities –hive-import –hive-overwrite
–null-string ‘\\N’ –null-non-string ‘\\N’ –hive-table emp.cities -m 1
Ans: Sqoop can import individual tables or entire databases into HDFS. The data is store in the native directories and files in the HDFS file system.
Ans: Sqoop uses MapReduce to import and export the data, which provides parallel operation as well as fault tolerance.
Ans: Load directly into Hive tables, creating HDFS files in the background and the Hive metadata automatically
1.Hive Tables
2.HDFS files
3.Hbase (HBase is an open-source, distributed, versioned, non-relational database modeled after Google’s Bigtable)
Ans: Apache Sqoop is a tool use for transferring data between Apache Hadoop clusters and relational databases.
Sqoop was originally develope by Cloudera. The name ‘Sqoop’ is a short form for ‘SQL-to-Hadoop’.
Sqoop can import full or partial tables from a SQL database into HDFS in a variety of formats. Sqoop can also export data from HDFS to a SQL database.
Ans: Apache Sqoop is a command-line utility that has various commands to import data, export data, list data, etc. These commands are called tools in Sqoop. Following is the basic command-line syntax for using Apache Scoop.
Ans: You can import the data from a single table using the tool or command ‘import –table’. You can use the option ‘–warehouse-dir’ to import the data into ‘customerdata’ HDFS directory.
$ sqoop import –table customers
–connect jdbc:mysql://myhostname/interviewgrid
–username myusername –password mypassword
–warehouse-dir /customerdata
Ans: We can control the number of mappers by executing the parameter –num-mapers in sqoop command. The –num-mappers arguments control the number of map tasks, which is the degree of parallelism use. Start with a small number of map tasks, then choose a high number of mappers starting the performance may down on the database side.
Syntax: -m, –num-mappers
Ans: The Sqoop jar in classpath should be includ in the java code. After this, the method Sqoop.runTool () method must be invoke. The necessary parameters should be create to Sqoop programmatically just like for the command line.
Ans: It is a tool using which Sqoop hosts a shared metadata repository. Multiple users and/or remote users can define and execute saved jobs (created with sqoop job) defined in this metastore.
Clients must be configure to connect to the metastore in sqoop-site.xml or with the –meta-connect argument.
Ans: A sqoop merge is a tool that combines two different datasets that maintain the only version by overwriting the entries in an older version of a dataset with new files to make it the latest version dataset. There happens a process of flattening while merging the two different datasets which preserves the data without any loss and with efficiency and safety. In order to perform this operation merge key command will be use like “–merge-key”