Pull the client records from the source (a webhook trigger in this example) and create the table (if it doesn't exist) in the Redshift database. Insert new rows: Inserts one or more rows into your chosen Redshift database.įind rows: Finds and reads the rows from your chosen database based on the specified criteria. Run SQL query: Executes a raw specified SQL query on the chosen database. This example demonstrates the following operations of the Redshift connector: Once the records are added, the workflow continues to read the records from the selected Redshift table. If it does not, then the workflow creates a new table and then adds the received records to that table. Upon receiving the records, the workflow checks whether the table we want to add these records to already exists in Redshift. Once this is done, copy and paste the URL into said 'Host' field.īelow is an example of how you could potentially use the Redshift connector to insert new records into the Redshift database and read all the records from said database. You will need to edit the given URL before adding it to your Tray.io authentication model.įor example, if your 'Endpoint' URL is: .:5427/dev, users will need to remove everything after :5427 in order to have valid information for the 'Host' field. The Host name is the 'Endpoint' listed within the 'General informtion' section. From the available list of clusters, select the one that you're trying to connect.įirstly, it asks for 'Host'. In order to get details about your reshift cluster, log on to AWS and head over to the Clusters screen in your Amazon Redshift account. The first page will ask you to name your authentication, and state which type of authentication you wish to create ('Personal' or 'Organisational').Īs you can see, the next page asks you for the details about your redshift cluster ('Host', 'Port', 'Database', and 'Schema') and your credentials ('User' and 'Password'). Each node then has its size stored in this array – you could ask "What is the maximum node size over all clusters?".This will result in a Tray.io authentication pop-up window. In this example, I am assuming a table clusters where each row represents a cluster of "things" and each cluster consists of many nodes modeled as a JSON array. If you want to follow along the queries and play with the data, I created a Gist which has all the queries to create the dummy tables and that fills them in with test data. In October, RedShift added new functions to work with JSON 1 but the support is missing something like Hive's explode() or Postgres' unnest() functions to expand an array from one column into one row for each element.Īs a work-around, I came up with a simple hack: Joining the JSON array with a predefined sequence of integers and then extracting the element at each index into a new relation through that join. However, when running analytical queries, there is no out-of-the box way to join on "nested data" inside of arrays so up until now this data was very hard to use. Some of the data we store in RedShift contains JSON arrays. I hit a limit when I needed table-generating functions but found a work-around. However, its SQL dialect has some limitations when compared to Hive or PostgresSQL. Expanding JSON arrays to rows with SQL on RedShiftĪmazon's RedShift is a really neat product that solves a lot of our problems at work.
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