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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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2 Scaling-Up And Scaling-out QlikView's Ideas! That You Can Never Miss

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In scale-up architecture

A single server is used to serve the QlikView applications. In this case, as more throughput is required, bigger and/or faster hardware (e.g. with more RAM and/or CPU capacity) are added to the same server.

Scale-up
The Scale-up architecture


In scale-out architecture

More servers are added when more throughput is needed to achieve the performance necessary. It is common to see the use of commodity servers in these types of architectures. 

As more throughput is required new servers are added, creating a clustered QlikView environment. In these environments, QlikView Server supports load sharing of QlikView applications across multiple physical or logical computers. 

QlikView load balancing refers to the ability to distribute the load (i.e. end-user sessions) across the cluster in accordance with a predefined algorithm for selecting which node should take care of a certain session. QlikView Server version 11 supports three different load balancing algorithms.

Below is a brief definition of each scheme. Please refer to the QlikView Scalability Overview Technology white paper for further details. 

Scale-out
The scale-out Architecture
 
Random: The default load-balancing scheme. The user is sent to a random server, no matter if the QlikView application the user is looking for is loaded or not on a QlikView Server. 
 
Loaded Document: If only one QlikView Server has the particular QlikView application loaded, the user is sent to that QlikView Server. If more than one QlikView Server or none of the QlikView Servers have the application loaded, the user is sent to the QlikView Server with the largest amount of free RAM. 

CPU with RAM Overload: The user is sent to the least busy QlikView Server. Please note that this report does not go into detail on when to use and how to tune different load balancing algorithms for best performance. 

Cluster test executions presented in this report has been run in an environment configured with a better performing scheme for certain conditions of a particular test.

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