Step 3 : Connect to the MySQL using Python and create a DatabaseĬreate a connection object to connect to MySQL, The connect() constructor creates a connection to the MySQL and returns a MySQLConnection object.Ĭonn = nnect(host='localhost', ur username, passwordĬursor.execute("CREATE DATABASE employee") You'll need to change the path name to reflect the location where the CSV file is stored on your computerĮmpdata = pd.read_csv('C:\\Users\\XXXXX\\empdata.csv', index_col=False, delimiter = ',')
MYSQL CREATE TABLE FROM CSV CODE
Here is the code that I used to import the CSV file, and then create the DataFrame. Next, import the CSV file into Python using the pandas library. Note: the above employee csv data is taken from the below link employee_data Step 2: Import the CSV File into the DataFrame. For example, I prepared a simple CSV file with the following data: To begin, prepare the CSV file that you'd like to import to MySQL.
Use data source CREATE TABLE student ( id INT, name STRING, age INT ) USING CSV -Use data from another table CREATE TABLE student_copy USING CSV AS SELECT * FROM student -Omit the USING clause, which uses the default data source (parquet by default) CREATE TABLE student ( id INT, name STRING, age INT ) -Use parquet data source with parquet storage options -The columns 'id' and 'name' enable the bloom filter during writing parquet file, -column 'age' does not enable CREATE TABLE student_parquet ( id INT, name STRING, age INT ) USING PARQUET OPTIONS ( '' = 'true', '. Input query, to make sure the table gets created contains exactly the same data as the input query. Spark will create a default table location for you.įor CREATE TABLE AS SELECT, Spark will overwrite the underlying data source with the data of the An exception is file source such as parquet, json. In general CREATE TABLE is creating a “pointer”, and you need to make sure it points to somethingĮxisting.
Read/write table “foo”, you actually read/write table “bar”. For example, you can createĪ table “foo” in Spark which points to a table “bar” in MySQL using JDBC Data Source. The table is populated using the data from the select statement.Ī Data Source table acts like a pointer to the underlying data source. Path to the directory where table data is stored, which could be a path on distributed storage like HDFS, etc.Ī list of key-value pairs that is used to tag the table definition. Specifies buckets numbers, which is used in CLUSTERED BY clause. If not specified, ASC is assumed by default. Optionally, one can use ASC for an ascending order or DESC for a descending order after any column names in the SORTED BY clause. NOTE: Bucketing is an optimization technique that uses buckets (and bucketing columns) to determine data partitioning and avoid data shuffle. Partitions created on the table will be bucketed into fixed buckets based on the column specified for bucketing. Partitions are created on the table, based on the columns specified. Options of data source which will be injected to storage properties. Data source can be CSV, TXT, ORC, JDBC, PARQUET, etc. Specifies a table name, which may be optionally qualified with a database name.ĭata Source is the input format used to create the table. For example, you can write COMMENT table_comment after TBLPROPERTIES. Note that, the clauses between the USING clause and the AS SELECT clause can come inĪs any order.
0 kommentar(er)
