I have been using Redis as a swiss army knife database, both at work, and for hobby projects. So I decided to attend RedisConf this year. Low cost of entry, more tech talks than marketing spiels, and a chance to meet Salvatore Sanfilippo, the creator of Redis, were the big draw to the event.
The event was a great introduction to upcoming and lesser used redis features (used by me that is). It was a fun and eye opening window into a lot of different domains and use cases. When all you sell is cheese, the concept of sour cream is interesting..
Some that blew my mind were:
About a decade ago, a simple classifier on an MPI Beowulf cluster took few weeks to implement. These days, I could use a python ML classifier to do the same thing in just few hours.
As complicated technology becomes more pervasive, the entry points to the tech itself gets easier, which further paves way for more complicated technology. Redis has already enabled a lot of industries, which are further revolutionizing everything from healthcare to robotics.
We live in interesting times.
The event was a great introduction to upcoming and lesser used redis features (used by me that is). It was a fun and eye opening window into a lot of different domains and use cases. When all you sell is cheese, the concept of sour cream is interesting..
Some that blew my mind were:
- IoT: Prior to this conference, I had a limited understanding of it's use-cases. IoT, to me, was just using a web-controlled device. A light-bulb, or a smart fridge, that'd ping me if the milk ran out etc. I.e. a very Silicon Valley centric first world problem solver.
The use-cases that Laura Merling provided in her talk, just blew me away: - Disney MagicBands: When you navigate your way through disney parks, your hotel automatically knows that you are coming, your door automatically unlocks & most importantly, if you get dangerously close to a moving ride, the ride adjusts itself (can stop) to get you out of harm's way!
- The oil & gas industry: The oil rigs generate a lot of real time information on the equipment’s performance and health — pressure, temperature, flow rates and multitudes of other variables. IoT based solutions provide real time analytics for predictive and preventative measures. This link explains this further.
- Factory safety: Remember the incident, when an automotive factory robot arm crushed a factory operator into a metal plate. This happened because the robot arm mistook the person as another part of the machinery. There have been around 26 deaths in the past 30 years in US alone. This number, although low, can be further brought down by judicial use of IoT tech (just like Disney's magicBand).
- Home Automation: Home automation industry is fairly nascent right now. There is an app to control the lights, another one to control your locks, another one for your TV and so on. There needs to be push from the consumers for tighter integration of products for arbitrary control. E.g. to turn down the volume of the music system if someone is sleeping in the other room, or turn on the lights when the doors are unlocked, and so on.
- Distributed systems & the rise of DevOps: There were couple of talks by companies like Netflix and Scopely. The amount of data served and generated by companies these days is monumental.
Avram Lyon from Scopely gave a behind the scenes look at the data usage. He talked about a simple mobile game where the goal is to hit oncoming zombies with some objects. His game has a modest 100,000 users active at any given time. To generate insights into the game (E.g. People in California like to throw rocks at zombies, whereas people in New York like to hurl trash-cans at them, or that no-one seems to like throwing hammer at zombies), the amount of data generated and recorded is huge. This is used to provide the development and marketing teams with actionable intelligence. Few TBs of data is not huge by today's standards, and conventional databases are ill-suited for this endeavor. Redis, along with it's other supporting actors lends well to this use-case.
The conference was filled with 2-4 member teams who had set up massively scaled systems within 5-6 months using FOSS(kafka, spark, redis etc) on el-cheapo AWS cluster. Horizontal automatic scaling being the key takeaway. One doesn't need to be a star programmer to build these systems. The architecture has matured enough that systems can be deployed with minimal effort (relatively).
Another emergent database concept was Eventual consistency. Unlike traditional database systems, that dictate that the view of the databases need to be the same for everyone, eventually consistent databases are like.. meh, it's not the end of the world if are able to access the new season of "House of Cards" few minutes after someone else. Heuristics are further tied to extrapolate behavior of the system to get massive scalability. Amazon, Netflix etc have this kind of systems. Take a look at this to get more detailed view.
- Hardware advancement: Traditionally, Redis has been used as a memcached replacement. Something that sits in-front of traditional sql databases. Apps like Twitter et al would store the hot keys in a massively distributed redis cluster (in order of TBs) and would later save these to some backend stores. This adds another layer of complexity. With the advent of flash memory, the backend stores typically now sit on flash memory. However, the newer NVDIMMs & NVMs perform orders of magnitude better than flash memory at just 1/10th the price of RAM. With the closing gap between RAM & Flash, the functionality and scalability of Redis is increasing by leaps and bounds.
- Redis Modules: Usual databases are just dumb datastores. Their main utility is to store the data efficiently in the memory and provide an easy way to retrieve it. All the intelligence to consume that data goes into the client code. Salvatore announced that starting 4.0, Redis would support loadable modules that can be used to offload client functionality to Redis. You could use this to keep track of counter rates or a running average or use simple ML algos to automatically classify the images being saved on the DB or apply some sort of filter on them. The possibilities are endless.
About a decade ago, a simple classifier on an MPI Beowulf cluster took few weeks to implement. These days, I could use a python ML classifier to do the same thing in just few hours.
As complicated technology becomes more pervasive, the entry points to the tech itself gets easier, which further paves way for more complicated technology. Redis has already enabled a lot of industries, which are further revolutionizing everything from healthcare to robotics.
We live in interesting times.