In our previous Fun with SQL post on the Citus Data blog, we covered window functions. Window functions are a special class of function that allow you to grab values across rows and then perform some logic. By jumping ahead to window functions, we missed so many of the other handy functions that exist within Postgres natively. There are in fact several hundred built-in functions. And when needed, you can also create your own user defined functions (UDFs), if you need something custom. Today we’re going to walk through just a small sampling of SQL functions that can be extremely handy in PostgreSQL.

It’s a common question we get at conferences, on calls, in meetings. “Citrus”, “Citius”, “Citus”, is that how you pronounce it? The quick and short of it is, we’re not named after a fruit. You pronounce it like site-us.

Most tend to leave it there, without wondering further. But a few do enquire as to the meaning. Citus’ name comes from the Olympic Motto “Citius, Altius, Fortius” which is Latin for “Faster, Higher, Stronger.” Our goal at Citus Data is to build a database that’s fast both for transactional and analytical workloads.

Many companies generate large volumes of time series data from events happening in their application. It’s often useful to have a real-time analytics dashboard to spot trends and changes as they happen. You can build a real-time analytics dashboard on Postgres by constructing a simple pipeline:

1. Load events into a raw data table in batches
2. Periodically aggregate new events into a rollup table
3. Select from the rollup table in the dashboard

For large data streams, Citus (an open source extension to Postgres that scales out Postgres horizontally) can scale out each of these steps across all the cores in a cluster of Postgres nodes.

One of the challenges of maintaining a rollup table is tracking which events have already been aggregated—so you can make sure that each event is aggregated exactly once. A common technique to ensure exactly-once aggregation is to run the aggregation for a particular time period after that time period is over. We often recommend aggregating at the end of the time period for its simplicity, but you cannot provide any results before the time period is over and backfilling is complicated.

work_mem is perhaps the most confusing setting within Postgres. work_mem is a configuration within Postgres that determines how much memory can be used during certain operations. At its surface, the work_mem setting seems simple: after all, work_mem just specifies the amount of memory available to be used by internal sort operations and hash tables before writing data to disk. And yet, leaving work_mem unconfigured can bring on a host of issues. What perhaps is more troubling, though, is when you receive an out of memory error on your database and you jump in to tune work_mem, only for it to behave in an un-intuitive manner.

Earlier this week as I was waiting to begin a talk at a conference, I chatted with someone in the audience that had a few questions. They led off with this question: is Citus a good fit for X? The heart of what they were looking to figure out: is the Citus distributed database a better fit for analytical (data warehousing) workloads, or for more transactional workloads, to power applications? We hear this question quite a lot, so I thought I’d elaborate more on the use cases that make sense for Citus from a technical perspective.

Before I dig in, if you’re not familiar with Citus; we transform Postgres into a distributed database that allows you to scale your Postgres database horizontally. Under the covers, your data is sharded across multiple nodes, meanwhile things still appear as a single node to your application. By appearing still like a single node database, your application doesn’t need to know about the sharding. We do this as a pure extension to Postgres, which means you get all the power and flexibility that’s included within Postgres such as JSONB, PostGIS, rich indexing, and more.

Today we continue to explore all the powerful and fun things you can do with SQL. SQL is a very expressive language and when it comes to analyzing your data there isn’t a better option. You can see the evidence of SQL’s power in all the attempts made by NoSQL databases to recreate the capabilities of SQL. So why not just start with a SQL database that scales? (Like my favorites, Postgres and Citus.)

Today, in the latest post in our ‘Fun with SQL’ series (earlier blog posts were about recursive CTEs, generate_series, and relocating shards on a Citus database cluster), we’re going to look at window functions in PostgreSQL. Window functions are key in various analytic and reporting use cases where you want to compare and contrast data. Window functions allow you to compare values between rows that are somehow related to the current row. Some practical uses of window functions can be:

• Finding the first time all users performed some action
• Finding how much each users bill increased or decreased from the previous month
• Find where all users ranked for some sub-grouping

Today, we’re excited to announce the latest release of our distributed database, Citus 7.4! Citus scales out PostgreSQL through sharding, replication, and query parallelization.

Ever since we open sourced Citus as a Postgres extension, we have been incorporating your feedback into our database. Over the past two years, our release cycles went down from six to four to two months. As a result, we have announced 10 new Citus releases, where each release came with notable new features.

Shorter release cycles and more features came at a cost however. In particular, we added new distributed planner and executor logic to support different use cases for multi-tenant applications and real-time analytics. However, we couldn’t find the time to refactor this new logic. We found ourselves accumulating technical debt. Further, our distributed SQL coverage expanded over the past two years. With each year, we ended spending more and more time on testing each new release.

In Citus 7.4, we focused on reducing technical debt related to these items. At Citus, we track our development velocity with each release. While we fix bugs in every release, we found that a full release focused on addressing technical debt would help to maintain our release velocity. Also, a cleaner codebase leads to a happier and more productive engineering team.

We spend a lot of time with companies that are growing fast, or planning for future growth. It may be you’ve built your product and are now just trying to keep the system growing and scaling to handle new users and revenue. Or you may be still building the product, but know that an even moderate level of success could lead to a lot of scaling. In either case where you spend your time is key in order to not lose valuable time.

As Donald Knuth states it in Computer Programming as an Art:

“Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.”

With the above in mind one of the most common questions we get is: What do I need to do now to make sure I can scale my multi-tenant application later?

We’ve written some before about approaches not to take such as schema based sharding or one database per customer and the trade-offs that come with that approach. Here we’ll dig into three key steps you should take that won’t be wasted effort should the need to scale occur.

Common Table Expressions (CTEs) are a powerful construct within SQL. In day to day conversation, you may hear CTEs referred to as WITH clauses. You can think of CTEs as similar to a view that is materialized only while that query is running and does not exist outside of that query. CTEs can be very useful building blocks for allowing your large SQL queries to be more readable. But, they can also be used recursively allowing you to create some very complex queries without having to drop down to a procedural language like plpgsql or plv8.

Hi. I work as a data architect in San Francisco and I’m auditing Dr. Jones class to stay up to date on the latest technologies and she mentioned you might be able to help me before I get too deep into the design of a new system.

• I would be happy to help. Can you give me an overview of where you’re at?

My default was just to use Postgres. I had a few questions on what schema designs might make most sense.

• Well I’m working with more interesting data architectures, not sure I can help you much with Postgres. Really getting excited about what’s possible with neomodern data architectures, they make it so my app devs can build any feature their hearts desire.

I thought your expertise used to be relational databases?

• It was, but neomodern data architectures are better. Neomodern data architectures allow it so app devs can build any feature they like without having to think about data models. Really, it’s the future of databases.