Heimdall Proxy Standard Edition
Heimdall Data | 24.09.06.1Linux/Unix, Amazon Linux 2 - 64-bit Amazon Machine Image (AMI)
Really powerful tool.
“The Heimdall proxy gave us both AWS cost savings and technical benefits. With automated caching, we reduced our RDS instance in half as SQL traffic was offloaded. The proxy's web interface was simple yet powerful. It had the functionality you needed to control queries (e.g. routing, stack trace, triggers). Installation through the AWS Marketplace was simple with a couple of clicks. The configuration was quick and easy due to the AWS Configuration Wizard.
We experienced a query response improvement of 347% and the database time saved was 78% and for some queries we had a cache hit ratio of 83%! Fantastic! One of the key benefits was that we did not have to change one line of code. We simply changed our software’s connection string to point to the Heimdall proxy and then immediately saw a reduction in RDS resources consumption!
The support team was very responsive and they were very knowledgable to assist you until the problem was resolved.”
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Great Software/Easy to implement/Huge performance boost with no code changes
We moved a small genomics Java based website to AWS and wanted to implement SQL caching and Read/Write splitting for the database that we had migrated to Aurora MySQL Compatible. Heimdall Data allowed us to do both with no code changes in an afternoon and our overall page loading performance improved 93% over a 2 week testing period. Support reached out during our trial and provided very useful guidance on how best to implement a solution that would work for us. Great software and great support that has allowed us to improve performance and focus on developing new features not changes to support caching and read/write splitting.
Heimdall Data --> a hidden gem!
We learned about Heimdall Data while looking for an easy-to-integrate caching solution for our Java application. We were having some growing pains with lots of new customers and we weren't ready for the higher volumes. We knew we had to do something - fast! To our surprise, implementing Heimdall was easy. We didn't have to do any code changes to our application to take advantage of their technology.
We deployed Heimdall Data to our production environment on AWS and soon starting seeing ~80% cache hit rates! We immediately noticed an improvement in response times and the load on the database was substantially lower. We have been running Heimdall Data in production for over 6 months and have had great results helping us scale without having to hire a DBA or increase AWS hardware specs ($$).
Other positives - Heimdall has a very good reporting system which provides monitoring and analytics of database queries. This helps identify any issues and optimize our database queries. In addition Heimdall provides very good support. They have very good expertise on AWS and some of their advice helped improve performance of our product.
I highly recommend them!
Great Product
We were very glad to find Heimdall Data and eventually implement their product. Their platform handles the caching layer for database queries, among many other things. The only comparable solution we came across in the market is Pgpool, but Heimdall Data has proven to be in a class of its own.
Pgpool handles connection pooling, read/write splitting, and manual caching, However, it is all manual and requires code changes. Pgpool also does not support SELECT FOR UPDATE and several other options we found readily available through Heimdall Data.
We first tested Pgpool and very quickly realized that it would require extensive work to update, maintain and scale. When we began testing with Heimdall Data we were able to see huge improvements in load testing very quickly due to its automation. We were able to increase our test throughput about 5-7x what original performance tests had shown. We continued to test and learned all about the rules engine, explain plans, and analytics which allowed us to quickly identify several places for optimization. We could easily see query response sizes, response times, and query count, among others.
Heimdall data has allowed us to see into the database queries and transaction times on both the application and Amazon Aurora. The level of monitoring and analytics that Heimdall Data provides allowed us to find and identify issues that only a seasoned DBA would be able to gather previously.
We deployed Heimdall Data to our production environment and have been seeing between 60-70% cache hit rate overall. Immediately we noticed an improvement in response times and the benefit of our application and database load being reduced greatly. We have been running Heimdall Data in production for over 2 months and have had great results. This has been an invaluable tool for helping us scale and allowing us to keep resources low.
Overall, We have been very satisfied with Heimdall Data and can highly recommend it.
Heimdall Data auto-caching came to our rescue
Performance is a major concern for any application developer and organization. Often enough, people don't worry about it until revenue, organic traffic and session time start to dip or when they see high infrastructure costs on P&L. This needs to be something everyone focuses on up front, not when the ceiling is falling down. When performance needs to be improved, caching is often the first step taken. The best caching is the type used without involving your application.
Heimdall Data auto-caching came to our rescue. We were able to develop our application 10x faster without additional coding and worry about what/when to cache/expire to improve our time to market. The analytic tool is awesome! It helped us find performance bottlenecks very quickly. We were able to see SEO ranking, user session times, search engine page crawled per day improve day after day while saving for development and infrastructure cost. We spent less time finding bottlenecks, coding, and reduced cost on databases. RDS is our most expensive item in our infrastructure and we were able to reduce our databases cost by using Hemidall Data analytic tools to find/fix the bottleneck very quickly and caching more.