We live in an age of an abundance of database choices. The databases have trade-offs in terms of work to implement, rigidity vs flexibility, write performance, read performance, query performance, maintenance, support, robustness, security, and so on. It seems that many databases can be tuned to meet requirements, but it may require hiring an expert to get the most out of it, or to tell you that a given database may not be the right fit.
I recently learned of the existence of MemSQL, AeroSpike, Cockroach DB, Clustrix, VoltDB and NuoDB. Several of these came to my attention from reading an InfoWorld article, although what I cover here doesn’t exctly overlap.
- Commercial only, with gratis community edition.
- It supports a json column type, and can index, query and update data within the json.
- Keen insights from their team of engineers. See http://blog.memsql.com/cache-is-the-new-ram/. “Throughput and latency always have the last laugh.” I.e. locality still matters.
- “As various NoSQL databases matured, a curious thing happened to their APIs: they started looking more like SQL. This is because SQL is a pretty direct implementation of relational set theory, and math is hard to fool.”
- “We realized that caching cost at least as much RAM as the working set (otherwise it was ineffective), plus the nearly unbearable headache of cache consistency.”
- AGPL NoSQL db, led by a former CEO of Salesforce.com. http://stackoverflow.com/questions/25208914
- key-value store, although since it supports nested key-values, it may be somewhat equivalent to MongoDB’s schemaless json doc storage.
- Scaleable. Far better than Redis when it’s time to scale.
- Aerospike is reportedly faster than MongoDB (in 2014, that is)
- Needs fewer nodes than MongoDB, and so it reportedly costs less.
- APL 2.0
- scaleable (distributed)
- beta software
- Higher write latencies. Built on RocksDB from Facebook.
- Proprietary drop-in replacement for MySQL.
- 540 million transactions per minute.
- Higher write throughput than MongoDB (reportedly).
- Not a document store. It’s an RDBMS
- scaleable RDBMS.
- Millions of writes per second.
- AGPL 3.0 or commercial.
- Java & C++.
- Fixed, rigid schema — db downtime required to change schema.
- Upgrades require stopping the cluster.
- Stored procedures for queries.
- Michael Stonebraker is behind it.
- Interesting read: https://www.voltdb.com/blog/two-types-databases-defines-world-or-reacts-world
- ACID complaint, SQL RDBMS
- Memory centric
- Scaleable, without sharding. (how does that work?)
- More than 1 million transactions per second
- Flexible schema
- Java stored procedures
- Despite claims that it “automatically adjusts for optimal workload”, my guess is that one must monitor and tune it. Computer algorithms are smart… until they’re not.