Data Pipeline Load of CSV Data from S3

a common setup for loading files in a batch into {{ocient}} is to load from a bucket on {{aws}} s3 with time partitioned data often, you must perform a batch load repeatedly to load new files ocient uses data pipelines to transform each document into rows in one or more different tables the loading and transformation capabilities use a simple sql like syntax for transforming data this tutorial guides you through a simple example load using a small data set in csv format the data in this example comes from a test set for the {{metabase}} business intelligence tool prerequisites this tutorial assumes that the ocient system has network access to s3 from the loader nodes an ocient system is installed and configured with an active storage cluster (see the docid\ nneedy7yn8g1pennmamng guide) step 1 create a new database to begin, load two example tables in a database first, connect to a sql node using the docid 7uosju7ajx4yd61vqbhqu then, execute the create database sql statement for a database named metabase create database metabase; step 2 create table create the orders table in the new database first, connect to that database (e g , connect to jdbc\ ocient //sql node 4050/metabase ), and then execute this create table sql statement that specifies to create a table with these columns and a clustering index based on the user id and product id columns created at as a {{timekey}} timestamp that is not nullable id , user id , and product id as integers that are not nullable subtotal , tax , total , and discount as floating point numbers quantity as an integer create table public orders ( created at timestamp time key bucket(30, day) not null, id int not null, user id int not null, product id int not null, subtotal double, tax double, total double, discount double, quantity int, clustering index idx01 (user id, product id) ); the database creates the orders table, and you can begin loading data step 3 create a data pipeline create data pipelines using the create pipeline sql statement to load data, you first create a pipeline with the definition of the source, data format, and transformation rules using a sql like declarative syntax then, you execute the start pipeline command to start the load you can observe progress and status using system tables and views each ocient pipeline defines a single data source and the target table or tables into which data loads a data source includes the location of the source and filters on the source to define the specific data set to load this example loads data from two data sources, where each source is located in a directory on the same s3 bucket first, inspect the data that you plan to load each document has a format similar to this example csv file named orders csv id,user id,product id,subtotal,tax,total,discount,created at,quantity 1,1,14,37 65,2 07,39 72,null,2019 02 11t21 40 27 892z,2 2,1,123,110 93,6 1,117 03,null,2018 05 15t08 04 04 580z,3 3,1,105,52 72,2 9,49 2,6 42,2019 12 06t22 22 48 544z,2 in this case, ocient automatically transforms the data to the target columns using some sensible conventions in other cases, loads require some transformation most transformations are identical to functions that already exist in the sql dialect of the ocient system create a pipeline named orders pipeline for the orders data set from your database connection prompt use the s3 data source with endpoint https //s3 us east 1 amazonaws com , bucket ocient examples , and filter metabase samples/csv/orders csv specify the csv format with one header line load the data into the public orders table the select part of the sql statement maps the fields in the csv file to the target columns in the created table sql create pipeline orders pipeline source s3 endpoint 'https //s3 us east 1 amazonaws com' bucket 'ocient examples' filter 'metabase samples/csv/orders csv' extract format csv num header lines 1 into public orders select $1 as id, $2 as user id, $3 as product id, $4 as subtotal, $5 as tax, $6 as total, $7 as discount, $8 as created at, $9 as quantity; the pipeline has three main sections source — in this case, load data from s3 specify the endpoint and bucket the filter parameter identifies the file or files to load the load uses a single csv file options exist to add wildcards or regular expressions to isolate different file sets extract — set the format to csv files and note that there is one header line in the file this specification skips that row when the ocient system processes the file many other options exist for delimited data such as a record delimiter and field delimiter into select — choose the target table public orders and select the fields from the csv file the numeric index identifies each file field importantly, similar to other sql syntax, the first field in the file is $1 , not $0 each field maps to a target column using the as syntax after you successfully create the orders pipeline pipeline, execute the start pipeline command sql start pipeline orders pipeline; step 4 observe the load progress with your pipeline running, data immediately begins to load from the s3 files that you defined if there are many files in each file group, the load process first sorts the files into batches, partitions them for parallel processing, and assigns them to loader nodes you can check the pipeline status and progress by querying information schema pipeline status or by executing show pipeline status sql show pipeline status; output sql database name pipeline name table names status status message percent complete duration seconds files processed files failed files remaining records processed records loaded records failed \ metabase orders pipeline \["public orders"] running started processing pipeline orders pipeline 0 0 2 025824 0 0 1 0 0 0 after the status of the pipeline changes to completed , all data is available in the target table after a few seconds, the data is available for query in the public orders table select count( ) from public orders; output sql count( ) \ 18760 you can drop the pipeline with the drop pipeline orders pipeline; sql statement execution of this statement leaves the data in your target table, but removes metadata about the pipeline execution from the system related links docid\ l8tdfpfzzvzeyabc2h7bq docid 1 jhzrhblgnqucl6skiaq docid 7s5nztl8lolpwt2pcnjyz docid\ aimcafoydn2xf fgqssys docid\ ay3earmjsfi wkxd bwmv