I'm co-founder of a company, doing all of the technical work, and I too am using a very vanilla stack: Postgres, Elasticsearch, Redis, Memcache, Rails. However, having worked on massive scale-out projects before, I recognize where my pain points are going to be with my tech choices.
[1] I'm storing calendar data in postgres. This data is primarily key/value based, needs to be persistent, and on a per user basis potentially is tens of thousands of records. This is a medium term problem, and I'll likely move this data out of postgres and into a scale out solution sooner rather than later. I need relatively fast lookup times, but this is primarily a "high write, low read" scenario, that Cassandra is well suited for.
[2] I'm storing a bunch of text from blog posts, news articles, etc., in Postgres. I really dont' need the data to be in postgres, it's just a handy place to store it to synchronize with Algolia & Elasticsearch, where the true work happens. However, I dont' want to hold onto TBs & TBs of news articles in a postgres system. As that system scales out, it'll be unnecessarily expensive, as I don't need all the bells and whistles I'm getting from Postgres, for what is effectively a form of cold storage. This is a long term problem, and I likely won't tackle it for well over a year.
So, you're right, I'm not contemplating what DB to use, but I'm definitely aware of why I won't just leveraging the same database in perpetuity. Being focused on delivering customer value is vital, and that's my intent; however, scalability and cost structure do matter over the long term, so an understanding of where one will go is good as well.
Citus is more focused on scalable OLTP rather than OLAP scenarios, giving you essentially an automatically sharding postgres database. Heap does use it for analytics but that's more so because of the JSON support and indexing in postgres itself.
Can you share some docs with me around how citus is more OLTP based? Looking to be educated.
BigQuery streaming ingest also uses in-memory buffering into BigTable until it's written to disk, just like Cassandra with memtables flushed into on-disk SSTables.
Citus is more manual work than MemSQL and doesnt support columnstores native, although you can use it with their cstore extension to get both. It's just a different design giving you a postgres database that automatically shards vs MemSQL's many optimizations around HA, replication, and very fast analytics performance.
Try the features page: http://www.memsql.com/features/
This article had a nice overview. Somewhat outdated but mostly still up to date. https://lnkd.in/g4anCf5
I’ll say it seems precarious to me to mix and match row store in memory data with a columnar store. Is it never a performance issue to have these two systems potentially in contention with one another for resources?
Version 6 is even faster, you can try out the beta: http://www1.memsql.com/beta-6.3-features.html
Based on what ? There is no evidence or logic behind this.
If you know that you are producing say 1GB a day of data that you must store then it is sensible and prudent to select a technology that can support your needs a year or two from now.
And this idea that you always have resources to make a switch later on is rarely the case. The more common scenario is that a team is established to implement something, it moves into production and then the team either disbands or moves onto something else. Why do you think so many people try and plan for the future ?
In most enterprise companies at least it would be considered a failure to have a project that in a year's time required another business case just to replace the database that the team should've appropriately selected in the first place.
Designing for the future is a horrible choice because where do you stop?
Eg. Why are you sure that Cassandra, or whatever you choose, will be good enough for the future? Maybe you will need a Spanner type database or something even more big/consistent/feature-full.
Yes, I understand this. I would not want to get bogged down on infrastructure when trying to deliver early versions of a product. At the same time, I think database choice is one of the more important tech stack decisions you should make early on.
Cassandra also sounds like a good bet for the type of data I'm interested in storing (ML model inputs, so lots of key -> numerical value data). But my experience is mostly around RDBMS and some NoSQL. Hence the question. Thanks for the answer!
How big are the numerical value data? AFAIK Cassandra does like large amount of data per value. https://docs.datastax.com/en/cql/3.3/cql/cql_reference/refLi...
Also what do you expect the total amount of data to be?
BTW, I just recommended to start with PostgreSQL because it just works and will have your back no matter what ways you like to use retrieve you data.
However if you are absolutely sure you don’t require secondary indexes, joins, etc, and just need a KV database, Cassandra will work fine too.
Citus specifically cite analytics and write throughput as their strengths. I believe them. They do seem to charge for shard rebalancing and some other features.
Greenplum comes with an inbuilt data science toolkit (MADlib) and has a long history of dealing with intensive workloads. I don't know if there is a commercial version with extra features. I don't think so, but I haven't looked closely.
Greenplum also gave birth to HAWQ, which uses the Greenplum distributed/parallel query planner to plan queries over Hadoop.
Disclosure: I work for Pivotal, we do most Greenplum development.
As for elasticsearch, I have found it to be temperamental. The "out of the box" experience was usually unhappy. For logging I've seen people move to other tools (splunk, stackdriver) more than once.
2 TB/day is fairly easy... as long as you have a bit of experience AND one of the database that can scale horizontally AND the company buys the amount of hardware that is required.
StackDriver/Sumologic/logentries are cloud solutions, so obviously one doesn't have to deal with the database ever.
On the other hand, if I have structured-enough data, I'm going to prefer relational tools for it. Hammers and algebraic nails and so on.
If one is starting a new project, hence contemplating what DB to use, starting with an ACID db is a safe bet in most cases(unless of course they are already starting with a huge amount of data).
By the time the outgrow the ACID database they will have a better idea of what exactly they need and more importantly they will have the resources to make the switch.
BTW: riak is hopefully not dead yet, let's see what bet365 can do with it..