MySQL on Azure Performance Benchmark – ScaleGrid vs. Azure Database

Microsoft Azure is one of the most popular cloud providers in the world, and a natural fit for database hosting on applications leveraging Microsoft across their infrastructure. MySQL is the number one open source database that’s commonly hosted through Azure instances. While Microsoft offers their own Azure Database product, there are other alternatives available that may be able to help you improve your MySQL performance. In this blog post, we compare Azure Database for MySQL vs. ScaleGrid MySQL on Azure so you can see which provider offers the best throughput and latency performance. We measure latency in ms 95th percentile latency.

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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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2019 PostgreSQL Trends Report: Private vs. Public Cloud, Migrations, Database Combinations & Top Reasons Used

PostgreSQL is an open source object-relational database system that has soared in popularity over the past 30 years from its active, loyal, and growing community. For the 2nd year in a row, PostgreSQL has kept the title of #1 fastest growing database in the world according to the DBMS of the Year report by the experts at DB-Engines. So what makes PostgreSQL so special, and how is it being used today? We found the answers at the Postgres Conference in March where we surveyed PostgreSQL users, contributors, and SQL and NoSQL database administrators alike. In this free PostgreSQL Trends Report, we break down PostgreSQL hosting use across public cloud vs. private cloud vs. hybrid cloud, most popular cloud providers, migration trends, database combinations with Postgres, and why PostgreSQL is preferred over popular RDBMS alternatives.

Private Cloud vs. Public Cloud vs. Hybrid Cloud

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5 Steps to Benchmarking Managed NoSQL - DynamoDB vs Cassandra

This is a guest post by Ben Bromhead from Instaclustr

Deciding to use a managed NoSQL datastore is a great step in ensuring you run a fast, scalable and resilient application without needing to be an expert in highly available architecture. How do you know which technology is the best for your application? How do you know whether the provider's performance claims are true? You are putting your application on someone else’s infrastructure and that requires some hard answers about their claims.

To determine the suitability of a provider, your first port of call is to benchmark. Choosing a service provider is often done in a number of stages. First is to shortlist providers based on capabilities and claimed performance, ruling out those that do not meet your application requirements. Second is to look for benchmarks conducted by third parties, if any. The final stage is to benchmark the service yourself.

In this article we will show you how to run some preliminary benchmarks against two managed NoSQL systems. For this test we will compare Instaclustr and Amazon DynamoDB using the Yahoo Cloud Serving Benchmark (YCSB). Instaclustr provides managed Apache Cassandra hosting and DynamoDB is Amazons own managed key value store solution...

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Performance data for LevelDB, Berkley DB and BangDB for Random Operations

This is a guest post by Sachin Sinha, Founder of Iqlect and developer of BangDB.

The goal for the paper is to provide the performances data for following embedded databases under various scenarios for random operations such as write and read. The data is presented in graphical manner to make the data self explanatory to some extent.

• LevelDB:

  LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. Leveldb is based on LSM (Log-Structured Merge-Tree) and uses SSTable and MemTable for the database implementation. It's written in C++ and availabe under BSD license. LevelDB treats key and value as arbitrary byte arrays and stores keys in ordered fashion. It uses snappy compression for the data compression. Write and Read are concurrent for the db, but write performs best with single thread whereas Read scales with number of cores

• BerkleyDB:

  BerkleyDB (BDB) is a library that provides high performance embedded database for key/value data. Its the most widely used database library with millions of deployed copies. BDB can be configured to run from concurrent data store to transactional data store to fully ACID compliant db. It's written in C and availabe under Sleepycat Public License. BDB treats key and value as arbitrary byte arrays and stores keys in both ordered fashion using BTREE and un-ordered way using HASH. Write and Read are concurrent for the db, and scales well with number of cores especially the Read operation

• BangDB:

  BangDB is a high performance embedded database for key value data. It's a new entrant into the embedded db space. It's written in C++ and available under BSD license. BangDB treats key and value as arbitrary byte arrays and stores keys in both ordered fashion using BTREE and un-ordered way using HASH. Write, Read are concurrent and scales well with the number of cores

The comparison has been done on the similar grounds (as much as possible) for all the dbs to measure the data as crisply and accurately as possible.

The results of the test show BangDB faster in both reads and writes:

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The most common flaw in software performance testing

How many times have we all run across a situation where the performance tests on a piece of software pass with flying colors on the test systems only to see the software exhibit poor performance characteristics when the software is deployed in production? Read More Here...

Scaling MySQL on a 256-way T5440 server using Solaris ZFS and Java 1.7

How to scale MySQL on a 32 core system with 256 threads? Diagonal scalability in a box. An impressive benchmark that achieved more than 79,000 SQL queries per second on a single 4 RU server! Is this real? If so what is the role of good old horizontal scalability? The goals of the benchmark:

1. Reach a high throughput of SQL queries on a 256-way Sun SPARC Enterprise T5440
2. Do it 21st century style i.e. with MySQL and ZFS , not 20th century style i.e with OraSybInf... and VxFS
3. Do it with minimal tuning i.e as close as possible as out-of-the-box

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