Operations

Scalability, Performance, Reliability, and Operations Excellence

After Moving to Slack: Inbox Zero (at least twice per week)

TL,DR Version: Moving my entire Engineering organization to Slack and adopting a ChatOps collaboration mindset has reduced email volume over 95% and now enables us to resolve issues in 1/4th of the time.

I have always been a fan of using automation, web hooks, and chat-assisted operations to streamline work and enable collaboration across different locations. However, this traditionally required some Engineering and Operations (i.e., DevOps) investment in setting up collaboration servers, programming bots, etc. Slack has changed this, virtually eliminating all technical friction of moving to a ChatOps model. Here is our of how Slack enabled us to move to a ChatOps environment with far less email, faster response times, and greater overall productivity.

In late 2013, I joined a new company that—at the time—had no one on staff at the time with a DevOps-style background. After coming from several years of Chat-integrated operations, it felt like hitting the brakes. One night a few weeks later, I saw a Tweet by @mparca on February 9 about this new great service called Slack. I took a quick look and realized I could achieve everything that HipChat and Hubot did—with a simple push-button SaaS service. My initial set up (our organization, some channels, and hooks to Github) took less than 10 minutes. As it was free (for many features), I did not even need to process a Purchase Order (even better). I was off to setting ChatOps at a new place.

Initially, things went pretty slow. At the time, most of our tech stack and tools were hosted on-premise. Our chat tool of choice as a Skype (not a hook-friendly app). I got a few people to move to Slack, but not many.

Over time, as we implemented a full continuous integration and deployment chain, we added more and more hooks into Slack. First came a move to Atlassian (Cloud). Next Jenkins. Next Sentry. Then Ansible. Then Icinga. Then came custom RTM scripts for more complex things, such as letting our Data Scientists know they have left an idle PySpark context running for more than eight hours. What made this so easy was that everything but the custom RTM scripts could be done in less than five mouse-clicks (it is very helpful that so many collaboration and monitoring tools have enabled web hooks).

As we added more hooks, and started to bring more people onboard, I noticed an interesting shift. People joining our team began to just use Slack to communicate. One developer would come across an interesting new open source repo or article and share it with the rest in #general. Developers with DevOps privileges would jump on issues as soon as they saw a Sentry alert in #prod (saving the need to even text or phone the on-call engineer). Some people even answering questions in code review while they were doing things like waiting at the airport to depart on vacation.

Today our Engineering Teams (Product, Hardware, Software, Data, and Ops) all now primarily use Slack for communications. Most even use it in favor of texting. We Slack each other tickets that are ready for work, UAT, or release). We use group chats to have conversations to answer questions about stories, designs, bugs, and more. We use Slack channels integrated with Github for better code reviews. We use Slack to facilitate pair programming (and pair testing). Slack is now our default tool  for issue diagnosis, as sharing log messages, code snippets, and JSON is much clearer thanks to native markdown.

We achieve this with the following channel model:

  • One channel for each environment (so we can let people know if we are about to add nodes, run a load test, etc.). We have our respective Jenkins, Ansible, Icinga and Sentry hooks tied into each environment channel as well.
  • One channel for each code repository (to see PRs and conduct code reviews). We have aligned our JIRA projects with these to integrate tickets as well.
  • We have some basic team channels for more focused group conversations
  • We also have a #rm channel for simple-to-read log of what was released, when

As our organization moved to this model, life and work got easier in some rather visceral ways:

First, I have been able to dial down my notifications to only ping me when four things happen: I get a call, I get a text, I get a direct Slack message, or there is public Slack in a mission-critical channel. I no longer get endless interruptions, making me more productive at work (and more attentive in meetings and at home). If my phone does ping, it means there is something very important—which actually lets me react to these issues faster.

Second, my email volume is down over 95%. The bulk of the emails I now get are related to true business questions (vs. endless status messages and FYIs). As a result, I can answer email faster and now regularly hit “Inbox Zero” at least twice a week—while managing a 24x7xForever SaaS Engineering organization with follow-the-sun development and operations spanning California, Washington, Europe, and Asia.

Our full embrace of Slack did not happen overnight. It organically evolved over a period of about 18 months–a natural rate of adoption for organizational change. Because it was organic, we did have to institute policies  that forced usage. Instead we allowed our teams to naturally adopt Slack in ways that made work easier. I hope more organizations can make this transition as everyone could benefit from less email and fewer interruptions.

Natural adoption of Slack over other forms of communication would have happened if the usability was not as good as it is. One of my favorite features is how well Slack detects when I am no longer at my desk: if I walk down the hall, my phone chirps on key Slack messages; when I sit back down my phone stops and my laptop takes over.  This happens within seconds.

Oh BTW, we do all of this with the baseline free Slack account. That’s one less excuse to not give it a try.

PS – Want to work in an environment like this? Check us out.

A Practitioner’s Guide: Best NoSQL databases to solve 9 real-time transaction challenges

There are a lot of articles out there praising the features and performance of one NoSQL database over another. However, as a practitioner of the principle “pick the right tool for the job”, I thought I would write an article on picking the right NoSQL database for the transactional challenge you are facing—whether you are a startup, mid-sized enterprise or Fortune-500 company.

Before I get started, here are some caveats. First, I have no affiliation with any the companies who provide, or serve as custodians of, the databases outlined in this post. (I have, however, used them all.) Second, I am a big proponent in open source software. This is NOT based on a philosophical bent, but instead decades of experience scaling platforms for hundreds of millions of transactions per day. Third, I am big believer in ecosystems. When you big a technology with a good ecosystem, you get many benefits: you can hire people who no it, you can find lots of tools and libraries to enhance your use of it, and you benefit from patches to solve problems others have already found for your. Ecosystem size factors pretty heavily in my recommendations, as you may not want to bet your company on an untried technology.

Finally, I should address the elephant in the room. You will probably have someone in your company say something like this:

SQL can do anything NoSQL can do. It’s too risky to use it. We lose ACID compliance. We have a huge learning curve, etc.

Yes, relational technology is great for many uses. However, there are many situations where NoSQL technology can do things bigger, faster, at lower cost, and with less effort. Not just by a little bit, but often factors of a 1000x or more. Simply using RDBMS technology for every challenge is akin to using one hammer to for any type of nail, screw, peg, etc.

With this out of the way, let’s get started.

Challenge 1: Search (and Wildcard Searches)

You want to create a place where people can search for content in your site (and handle the idiosyncrasies of misspelling, grammar, and interchange of words like “one” for “1”). Similarly, you want to allow back office and enterprise application users to perform wildcard searches (such as all orders that contain “Nike”) as quickly as they can search for content on Google.

To solve these problems, use a search engine (which is technically a NoSQL database of inverted indices paired with lots scoring and fast spell checking functions). My favorite is ElasticSearch(ES). It is really fast and gives you some interesting capabilities you can use for specialized search (see Challenge #: Recommendation).  A second choice is Apache SOLR. SOLR is quite a bit slower than ES. However, it is included natively in many NoSQL distributions (HortonWorks, Cloudera, DataStax, etc.) If you already have gone through the cost of implementing these it makes sense to stick with Apache SOLR to get more value out of your investment.

BTW, never give users the option of wildcard or text searches in non-Search databases. It is a performance and scalability nightmare.

Challenge 2: Managing Versioned Pages of Information

You have a content management system for writers, journalists, etc. and want to maintain versions of their articles. You have an information management system for life sciences such as electronic medical records (EMR) or eClinical systems, and need to keep a copy of each version of a medical records page (or a clinical case report form, a.k.a. CRF).

To solve these problems use a document-oriented database. Instead of tracking associations of records (or paragraphs) to data to versions, you can simple store the whole document (often in JSON, preserving markup and annotation data) as a single entry for each version. My favorite is Amazon’s DynamoDB, based on its ease of setup and scalability. A close second for me is MongoDB (especially if you have a requirement for on-premise management). MongoDB would benefit by making it easier to setup multi-node clusters with encrypted data transfer. This still takes too much DevOps work.

Caveat: While Document DBs are great for storing pages as documents, I would not use them to present documents as pages to high-volume end users (e.g., for a content management system). Rather than present the ‘current published’ version of content from a database you should use some sort of cache. The easiest solution to setup is Amazon’s CloudFront CDN. However, if your scale and team are big enough Varnish is a more cost-effective solution.

Challenge 3: Managing Streams of Data, Events, and Actions

You want to manage long streams of information. You want to store all the events in a customer’s life cycle (for customer lifetime value management) and that activity is frequent. You want to store sensor events and GPS position reads for an asset (e.g., movement of freight in a reefer container). This challenge is getting more frequent given the explosion of data for mobile, sensors and social sharing events

The best tool for this is a wide-column database (very different from OLAP columnar analytic databases). Wide column databases bring two advantages: they let you store sparse information very efficiently (imaging storing all the topics a person could Tweet about or all the variables a sensor could capture). Second – with some good engineering work – they scale like mad and let you fetch this information back on 1/1000th of the time as traditional relational databases.

All the major columnar databases are children of Google’s BigTable database (that tells you something). My favorite wide-column database is Cassandra, especially if you want to use it for real-time streaming analytics and complex event processing. A far second choice for me is HBase (IMHO, HBase is essentially Cassandra–but with overly complicated ops management). However, if you already have a large bundled Hadoop installation from a major provider (e.g., HortonWorks, Cloudera, MapR) you probably already have HBase installed. HBase would also be my first choice if you company or groups is primarily a batch analytics shop (i.e., a big MapReduce data warehousing shop).

Challenge 4: Recommendation Engines

You want to recommend the best thing given other things your customer has viewed, liked or purchased. You want to recommend the best business to connect with based on given customers relationships with similar businesses. You want to find potential new customers based on their similarity to existing customers.

In reality, the best tools for recommendation are machine learning based. However, graph databases can make things easier machine learning algorithms or more basic recommendation features (such “users who liked this also like these”) to get the right information. Graph databases are still a bit of nascent market. The biggest leader is Neo4J. However, if you already have a Cassandra, HBase or (I am not sure why) Oracle Berkley DB installation you could simply install Titan to use this data. This would be my number one choice as Cassandra and HBase are fantastic ways to store very sparse info on customer preferences (e.g., all the things the viewed, liked or bought) thanks to their ability to support hundreds of thousands of columns per row—and read quickly along a column down a single column across many, many rows.

If you are just getting your feet wet in the recommendation engine space, you can also use ElasticSearch’s boost feature to highlight content for recommendation based on item scores. (These scores can be simple post-transaction calculations or fully robust machine learning driven scores). I once used ES to setup a recommendation engine in less than two weeks that actually beat a Google Search result. ES (and Apache SOLR) also have the “more like this” recommendation feature available out-of-the-box.

Challenge 5: Relationship- and Attribute-based Exploration

You want to create a site that users can browse to find “things like this” or “things related to that.” One example is searching for restaurants based on confluence of style, ingredients or similarity to restaurants I like (this could be equally applied to wine exploration on Lot18 or art exploration on Art.sy). I very popular use of this is browsing sports statistics, such as using Pro-Football-Reference.com for Fantasy Football team assembly.

For these challenges I would go immediately to a graph database. Out of the box, Neo4J would be my first choice. If I already had a big Hadoop or Cassandra installation, I would go to Titan.

Challenge 6: Knowledge Base Exploration

You want to create knowledge base to find the right “how to” content to address a problem, e.g., fix my account, figure out how to perform a feature in an app, etc. You could do this from customer facing site, call center, support desk, etc.

I spent many years working with companies who spent tens of millions of dollars using exotic database solutions to solve these problems. However, none of them worked as well as Search Engines. Search Engines are fast, scalable and handle all the vagaries of user behavior (such as spelling and grammar errors, use of “one” vs. “1”, etc.). In addition, thanks to Google, people are now “trained” to use Search to find answers to their questions.

ElasticSearch’s boost feature makes an amazing knowledge base manager. Search finds the relevant content and attributes such as views, votes, helpfulness ratings, etc. drive the boost score to raise answers up or down. The best example for this is StackOverflow (the developers crib notes for just about any problem).

Challenge 7: Sorted Catalog Browsing

You want to let users search a catalog of items, then switch to sort by things like price and rating or switch to browser by category and sub-category. You want to allow users to look at broad categories and drill into sub-categories to find items to buy. Basically how we all start looking for something at Amazon.com.

The starter technology for this is Search (again ElasticSearch is my recommendation of choice). You can use ES’s aggregation features to search and browser by category. However, as you get really big (i.e., millions of items in your catalog) or you wish to allow users to pivot from search results into structured browsing or explicit assignment of SKU items to categories you would add a columnar store database such as Cassandra or HBase. To see this action, search on ‘shirts’ at Amazon. Notice the phrase ‘Choose a Department to Sort’? This forces you to move from a search result to a columnar-based query based on nested keys. (To be completely clear, Amazon uses its own home-built tech for this, not Cassandra or even the stuff they expose on AWS for the rest of us to use. However, the principles are similar).

Challenge 8: Capturing Logs for Analysis

You want to capture logs of information for later forensic analysis. This could be for debugging, performance analysis, security and penetration analysis, or simply for audit compliance.

My number one choice for this is Riak. It is fast and simple to use. (The Mozilla Foundation uses this to capture all crash report data). A second choice for this is MongoDB as it is already bundled with many log analysis applications. (However, Mongo is more costly to scale than Riak). Another choice is simply streaming this data to a file store such as Apache HDFS to later interrogate with HBase, Hive, PrestoDB, or ElasticSearch.

Challenge 9: State Machine and Session Management

Ok, this one a bit technical. However sometimes you need to keep track of info for rapid lookup for later. You may want to keep track of the pages a person recently viewed on your site. You may want to keep a pre-approval status. You might want to hold a ticket for concert in temporary lock before purchase. You may want to keep the state of a customer or item for complex rule processing (e.g., keep track that I am exercising for fitness target alerts).

The best types of databases for these challenges are key-value Stores. My favorite is Redis. It is ease to setup and super fast. However, Redis is optimized for in-memory option. It is less optimal if you need guaranteed persistence of the information you want to store for rapid lookup. If you need persistence (such as storing pre-calculated algorithm or pre-qualification models), and have no pre-existing infrastructure, I recommend MemcacheDB. However, if you already are using Cassandra, HBase or DynamoDB you can simply use that as rapid key-value store (with persistence). You may even find this more cost-effective than setting up a Redis cluster even if you do not need persistence

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There are a lot of NoSQL technologies out there. The trick, like all things in life, is the pick the right tool for the job.