Entries in enterprise (4)

Tuesday
Oct292019

How to Improve MySQL AWS Performance 2X Over Amazon RDS at The Same Cost

How to Improve MySQL AWS Performance 2X Over Amazon RDS at The Same Cost

AWS is the #1 cloud provider for open-source database hosting, and the go-to cloud for MySQL deployments. As organizations continue to migrate to the cloud, it’s important to get in front of performance issues, such as high latency, low throughput, and replication lag with higher distances between your users and cloud infrastructure. While many AWS users default to their managed database solution, Amazon RDS, there are alternatives available that can improve your MySQL performance on AWS through advanced customization options and unlimited EC2 instance type support. ScaleGrid offers a compelling alternative to hosting MySQL on AWS that offers better performance, more control, and no cloud vendor lock-in and the same price as Amazon RDS. In this post, we compare the performance of MySQL Amazon RDS vs. MySQL Hosting at ScaleGrid on AWS High Performance instances.

TLDR

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Thursday
Jun272019

2019 Open Source Database Report: Top Databases, Public Cloud vs. On-Premise, Polyglot Persistence

2019 Open Source Database Report: Top Databases, Public Cloud vs. On-Premise, Polyglot Persistence

Ready to transition from a commercial database to open source, and want to know which databases are most popular in 2019? Wondering whether an on-premise vs. public cloud vs. hybrid cloud infrastructure is best for your database strategy? Or, considering adding a new database to your application and want to see which combinations are most popular? We found all the answers you need at the Percona Live event last month, and broke down the insights into the following free trends reports:

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Wednesday
Jul092014

Using SSD as a Foundation for New Generations of Flash Databases - Nati Shalom

“You just can't have it all” is a phrase that most of us are accustomed to hearing and that many still believe to be true when discussing the speed, scale and cost of processing data. To reach high speed data processing, it is necessary to utilize more memory resources which increases cost. This occurs because price increases as memory, on average, tends to be more expensive than commodity disk drive. The idea of data systems being unable to reliably provide you with both memory and fast access—not to mention at the right cost—has long been debated, though the idea of such limitations was cemented by computer scientist, Eric Brewer, who introduced us to the CAP theorem.

The CAP Theorem and Limitations for Distributed Computer Systems

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Tuesday
Sep162008

EE-Appserver Clustering OR Terracota OR Coherence OR something else?

Hi, I am very glad that this site exists, as I have learned more about clustering on this site than for quite some time reading stuff elsewhere. Oftentimes, one can find lots of material about clustering, but the practical real-life information is missing. Not so wih this site. I am currently planning the development of an application which has a lot of enterprise features and requirements. On the other side (if the tiny chance of success might strike us), this application would not be an in-house application of a financial institution, or something like that, but some kind of communit/web 2.0 web site. Thus it is an enterprise application with (hopefully, but surely unlikely) the user numbers of a social networking site. Each user initiated transaction involves huge resssources business logic wise (including insane amounts of encryption oprations). Of course, I do not intend to induldge into premature scaling, but to invest every minute I have into the implementation of business logic features. Nevertheless, I do not want to make some extremely bad choices which would force a complete reimplementation straight after the first tiny success - i.e. I want to start with the right technology and architecture, but wait with the implementation of the scalability and high availyability features. Because of the enterprise aspects of this software, my first thought was to use Java SE 6 and Java EE 5 technologies only in order to get all the JEE features and to be vendor independent at the same time. For implementation and testing purposes I thought of Glassfish v2UR2, Postgresql 8.3 and Solaris 10. As all of the major JEE-Appserver vendors advertise the clustering capabilities, I thought that this could not be a bad move. Hopefully, Glassfish would provide HA and scalability, if not there would always be Geronimo, JBoss, Weblogic, or Websphere. Now it seems that there are vast differences between different products: - JEE-Application servers are scaling only to some degree(?). It seems that JEE is almost exclusively used for enterprise applications like SAP ERP or applications at financial institutions? Therefore, there is no need for extreme scalability. - Terracotta seems to be very nice, as one do not have to learn the insanely huge JEE-technology stack, but can just write a mostly Java-SE-only threaded application(?). But Terracotta does not seem to scale very well either (bottleneck with write-operations caused by the master-worker architecture?) and we would be dependend on the future of the Terracotta Corporation. JEE on the other side is vendor neutral. - Oracle Coherence. This product seems to be the best distributed caching product and the holy grail of scalability(?). But it is oracle-expensive. Absolutely nothing for a tiny start-up with no financing. JEE is vendor neutral and thus possibly much cheaper. Do you think that it is possible that one could produce a JEE-Architecture which could provide massive scalability (many hundreds of AppServer) using only the Glassfish clustering features? Or am I on a completely wrong track? Do we have to plan for Oracle Coherence usage? Are there other possibilities? Thanks a lot for any opinions or hints! regards, mike

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