redshift alter table rename takes forever

REDSHIFT ALTER TABLE RENAME TAKES FOREVER HOW TO
REDSHIFT ALTER TABLE RENAME TAKES FOREVER UPDATE
REDSHIFT ALTER TABLE RENAME TAKES FOREVER CODE

You can think of Glue Spark jobs as a serverless Spark cluster on which you can run your code either through Python or Scala. It’s written in Scala and runs on top of the JVM, but don’t worry - there are frontends for it in different languages such as Python, Java, R or SQL. The in-memory aspect is crucial here, because it’s part of what makes the system very fast. Spark is a distributed system that you can use to perform In-Memory data transformation of large datasets. Spark PrimerĪpache Spark™ is a unified analytics engine for large-scale data processing.īefore we continue, maybe a quick primer on Spark. Python shell jobs allow you to run arbitrary Python Scripts in a Glue job without access to a Spark cluster. Spark Streaming is an extension of Spark with the niche use case of streaming data. Spark is usually used to perform the heavy lifting in terms of data transformation. These transformation jobs come in three varieties: Spark, Spark Streaming and Python shell. To create the transformation logic inside of the Glue jobs themselves, we typically take a different approach. Some people prefer creating them through the console, but infrastructure as code is considered as best practice here in order to have a reproducible environment and reduce the chance of accidental misconfigurations. The core components I described above are usually created through infrastructure as code frameworks such as CloudFormation, the Cloud Development Kit (CDK) or Terraform. I have visualized the components and their interaction in the following diagram.įrom a developer’s perspective there are different entrypoints when using Glue. Workflows enable you to define and visualize the order in which crawlers and jobs are supposed to be started to facilitate the data transformation. Orchestrating the different components to get a functional data processing pipeline can be done through Glue workflows. Integration in this context usually means that it provides information to these services about where external data stores are located and how they’re organized. It integrates with many different services such as Redshift, Athena, Lake Formation, QuickSight or EMR.

This data catalog is arguably the heart of Glue. You can direct it at different data stores and it tries to find out which data it holds, how it’s organized and how it can be accessed.Īn alternative to the crawler would be updating the data catalog through a Glue job.

REDSHIFT ALTER TABLE RENAME TAKES FOREVER UPDATE

Updating this data catalog could be a tedious and time-consuming manual process and that’s why there is another component of Glue that aims to initialize and update the data catalog.

If you come from a networking background, you can think of it like a router that redirects you to the target destination. It only contains metadata and points to the underlying data stores. One of the main differences between data catalogs and relational databases is that the data catalog doesn’t store any of your data. It’s organized in databases and tables, just like a relational database, which most of you might be familiar with. That’s why it makes sense to have a central system to keep track of these data stores and the data they hold. Sometimes different jobs extract from or write to the same data stores. In real life there are usually more than one of these processes or jobs as they’re called in Glue.

This is the origin of the ETL-acronym: extract-transform-load. This process starts with extracting data from one or more data stores, transforming it in some way and then loading it into one or more different data stores. The idea of Glue is to help you move data from point A to point B while also giving you the option to change the data in the process. This is not a deep dive into any specific topic, just an overview of the service, it’s components and things I consider useful information.

REDSHIFT ALTER TABLE RENAME TAKES FOREVER HOW TO

The developer experience, fundamental PySpark concepts and how to orchestrate complex processes. We’ll start with an introduction of the core components and then take a closer look at some aspects: I thought I’d write up what I wish I had known when I began maybe it will help others. In the beginning, I struggled to build a mental model of the different components, what they do and how they interact. It’s not really a single service, but more like an umbrella encompassing multiple capabilities. AWS Glue is a service I’ve been using in multiple projects for different purposes.

Redshift alter table rename takes forever