Example 1—Single Greenplum file server (gpfdist) instance on multiple NIC machine Creates a readable external table named ext_expenses using the gpfdist protocol. The files are formatted with a pipe ( | ) as the column delimiter. CREATE E
Example 1—Single Greenplum file server (gpfdist) instance on multiple NIC machine Creates a readable external table named ext_expenses using the gpfdist protocol. The files are formatted with a pipe ( | ) as the column delimiter.CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'gpfdist://etlhost-1:8081/*', 'gpfdist://etlhost-2:8081/*' ) FORMAT 'TEXT' (DELIMITER '|');
Example 2—Multiple Greenplum file server (gpfdist) instances Creates a readable external table named ext_expenses using the gpfdist protocol from all files with the txt extension. The files are formatted with a pipe ( | ) as the column delimiter and an empty space as null.
CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'gpfdist://etlhost-1:8081/*.txt', 'gpfdist://etlhost-2:8082/*.txt' ) FORMAT 'TEXT' ( DELIMITER '|' NULL ' ') ;
Example 3—Multiple secure Greenplum file server (gpfdists) instances Creates a readable external table named ext_expenses using the gpfdists protocol from all files with the txt extension. The files are formatted with a pipe ( | ) as the column delimiter and an empty space as null.
First, run gpfdist with the --ssl option. Then, execute the following command.
CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'gpfdists://etlhost-1:8081/*.txt', 'gpfdists://etlhost-2:8082/*.txt' ) FORMAT 'TEXT' ( DELIMITER '|' NULL ' ') ;
Example 4—Single Greenplum file server (gpfdist) instance with error logging Creates a readable external table named ext_expenses using the gpfdist protocol from all files with the txt extension. The files are formatted with a pipe ( | ) as the column delimiter and an empty space as null.
The external table is accessed in single row error isolation mode. An error table (err_customer) is specified. Any data formatting errors that are found in the input data will be discarded to err_customer, along with a description of the error. err_customer can later be queried in order to see the nature of errors and reload the rejected data after fixing the issues. If the count of badly formatted data rows on any specific segment is greater than five (specified as the SEGMENT REJECT LIMIT value), the entire external table operation will be aborted and no rows will be processed.
CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'gpfdist://etlhost-1:8081/*.txt', 'gpfdist://etlhost-2:8082/*.txt' ) FORMAT 'TEXT' ( DELIMITER '|' NULL ' ') LOG ERRORS INTO err_customer SEGMENT REJECT LIMIT 5;
Create the same readable external table definition as above, but with CSV formatted files:
CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'gpfdist://etlhost-1:8081/*.txt', 'gpfdist://etlhost-2:8082/*.txt' ) FORMAT 'CSV' ( DELIMITER ',' ) LOG ERRORS INTO err_customer SEGMENT REJECT LIMIT 5;
Example 5—TEXT Format on a Hadoop Distributed File Server (HDFS) Creates a readable external table named ext_expenses using the gphdfs protocol. The files are formatted with a pipe ( | ) as the column delimiter.
CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'gphdfs://hdfshost-1:8081/data/filename.txt' ) FORMAT 'TEXT' (DELIMITER '|');
Note: Only one data path is permitted with gphdfs. For examples of reading and writing custom formatted data on a Hadoop Distributed File System.
Example 6—Multiple file protocols in CSV format with header rows Creates a readable external table named ext_expenses using the file protocol.The wildcard specifications are not the same for all the files. The files are formatted in CSV format and have a header row.
CREATE EXTERNAL TABLE ext_expenses ( name text, date date, amount float4, category text, desc1 text ) LOCATION ( 'file://filehost:5432/data/international/*', 'file://filehost:5432/data/regional/*' 'file://filehost:5432/data/supplement/*.csv' ) FORMAT 'CSV' (HEADER);
Example 7—Readable Web External Table with Script Create a readable web external table that executes a script once per segment host:
CREATE EXTERNAL WEB TABLE log_output (linenum int, message text) EXECUTE '/var/load_scripts/get_log_data.sh' ON HOST FORMAT 'TEXT' (DELIMITER '|');
Example 8—Writable External Table that Writes to a File Create a writable external table named sales_out that uses gpfdist to write output data to a file named sales.out. The files are formatted with a pipe (|) as the column delimiter and an empty space as null.
CREATE WRITABLE EXTERNAL TABLE sales_out (LIKE sales) LOCATION ('gpfdist://etl1:8081/sales.out') FORMAT 'TEXT' ( DELIMITER '|' NULL ' ') DISTRIBUTED BY (txn_id);
Example 9—Writable External Web Table with Script Create a writable external web table that pipes output data received by the segments to an executable script named to_adreport_etl.sh:
CREATE WRITABLE EXTERNAL WEB TABLE campaign_out (LIKE campaign) EXECUTE '/var/unload_scripts/to_adreport_etl.sh' FORMAT 'TEXT' (DELIMITER '|');
Use the writable external table defined above to unload selected data:
INSERT INTO campaign_out SELECT * FROM campaign WHERE customer_id=123;
Example 10—Readable and Writable External Tables with XML Transformations Greenplum Database now can read and write XML data to and from external tables with gpfdist. For information on setting up an XML transform, see “Transforming XML Data” on page 178. The following code reads XML data into a table.
CREATE READABLE EXTERNAL TABLE prices_readable (LIKE prices) LOCATION ('gpfdist://127.0.0.1:8080/data/prices.xml#transform=prices_input') FORMAT 'text' (delimiter '|') LOG ERRORS INTO prices_errortable SEGMENT REJECT LIMIT 10;
The following code creates a writable external table that transforms the data in the Greenplum Database to XML.
CREATE WRITABLE EXTERNAL TABLE prices_readable (LIKE prices) LOCATION ('gpfdist://127.0.0.1:8080/data/prices.xml#transform=prices_input') FORMAT 'text' (delimiter '|');

InnoDBBufferPool reduces disk I/O by caching data and indexing pages, improving database performance. Its working principle includes: 1. Data reading: Read data from BufferPool; 2. Data writing: After modifying the data, write to BufferPool and refresh it to disk regularly; 3. Cache management: Use the LRU algorithm to manage cache pages; 4. Reading mechanism: Load adjacent data pages in advance. By sizing the BufferPool and using multiple instances, database performance can be optimized.

Compared with other programming languages, MySQL is mainly used to store and manage data, while other languages such as Python, Java, and C are used for logical processing and application development. MySQL is known for its high performance, scalability and cross-platform support, suitable for data management needs, while other languages have advantages in their respective fields such as data analytics, enterprise applications, and system programming.

MySQL is worth learning because it is a powerful open source database management system suitable for data storage, management and analysis. 1) MySQL is a relational database that uses SQL to operate data and is suitable for structured data management. 2) The SQL language is the key to interacting with MySQL and supports CRUD operations. 3) The working principle of MySQL includes client/server architecture, storage engine and query optimizer. 4) Basic usage includes creating databases and tables, and advanced usage involves joining tables using JOIN. 5) Common errors include syntax errors and permission issues, and debugging skills include checking syntax and using EXPLAIN commands. 6) Performance optimization involves the use of indexes, optimization of SQL statements and regular maintenance of databases.

MySQL is suitable for beginners to learn database skills. 1. Install MySQL server and client tools. 2. Understand basic SQL queries, such as SELECT. 3. Master data operations: create tables, insert, update, and delete data. 4. Learn advanced skills: subquery and window functions. 5. Debugging and optimization: Check syntax, use indexes, avoid SELECT*, and use LIMIT.

MySQL efficiently manages structured data through table structure and SQL query, and implements inter-table relationships through foreign keys. 1. Define the data format and type when creating a table. 2. Use foreign keys to establish relationships between tables. 3. Improve performance through indexing and query optimization. 4. Regularly backup and monitor databases to ensure data security and performance optimization.

MySQL is an open source relational database management system that is widely used in Web development. Its key features include: 1. Supports multiple storage engines, such as InnoDB and MyISAM, suitable for different scenarios; 2. Provides master-slave replication functions to facilitate load balancing and data backup; 3. Improve query efficiency through query optimization and index use.

SQL is used to interact with MySQL database to realize data addition, deletion, modification, inspection and database design. 1) SQL performs data operations through SELECT, INSERT, UPDATE, DELETE statements; 2) Use CREATE, ALTER, DROP statements for database design and management; 3) Complex queries and data analysis are implemented through SQL to improve business decision-making efficiency.

The basic operations of MySQL include creating databases, tables, and using SQL to perform CRUD operations on data. 1. Create a database: CREATEDATABASEmy_first_db; 2. Create a table: CREATETABLEbooks(idINTAUTO_INCREMENTPRIMARYKEY, titleVARCHAR(100)NOTNULL, authorVARCHAR(100)NOTNULL, published_yearINT); 3. Insert data: INSERTINTObooks(title, author, published_year)VA


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