Tag Archives: Titan

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.

Drone Commerce, Part 2: Global Internet Access

In Part 1 of this series, I looked at Amazon’s use of drones for same-day delivery. In this post, I will examine Google’s proposed use of drones for ubiquitous Internet access and near-Earth monitoring from the point of view of someone who has built things that fly, the software that controls them and large-scale Internet platforms.

The Drones of Titan

The drones created by Titan (now Google) Aerospace are quite different from the quadcopters you can buy online or the military UAVs featured so prominently in the news since 9-11. They are high-endurance drones intended to stay continuously aloft at 65,000’ (20 km) for 3 to 5 years. Running on solar-rechargeable batteries, they are designed to function as in-atmosphere satellites, providing communications (like COMSATs) or sensor-based observation (like weather and surveillance satellites).

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Packets of energy, not goods

Amazon’s is exploring use of drones to delivery physical goods. This brings on a host of complex aeronautic and air traffic challenges: the ability to carry payload while staying small enough to navigate inside cities; efficiently taking off and landing several times per day in the midst of wind gusts and other weather conditions; and the need to avoid trees, birds, power lines, buildings and host of other obstacles. Google’s drones avoid all of these challenges:

  • Flying at 65,000’ places them above all weather events and a majority of atmospheric turbulence. It also places them above birds, buildings, mountains and even commercial airline traffic
  • Staying aloft for years (or even just a few months) eliminates exposure to the highest-risk operation any non-military aircraft can do: takeoff or land. It also reduces equipment replacement costs and virtually eliminates re-fueling costs.
  • By transmitting and receiving photons (light and other electromagnetic waves) the drones do not need to be engineered to carry high payloads. They also do not need to be engineered for repeated loading and unloading of packages.

These changes significantly reduce operational risk and cost. From an engineer’s point of view, the technology is a great fit to its intended function. However…

Is this just and engineer’s fantasy?

Yes, the Google Drones appear to be great candidates for in-atmosphere satellites. However, keeping hundreds or thousands of drones aloft is a pricey enterprise with complexity akin to that of operating a mid-sized airport. Aren’t there technologies already available that already meet the needs these drones are intended to satisfy? Let’s look at the two commonly considered alternatives to help answer this:

Cellular (GSM/GPRS/3G/LTE/4G):

Cellular technology already exists in many, many parts of the world (even 95% of the people in Africa who live in areas with electrical power, live within coverage of cell towers). At first examination, using drones to give coverage to everyone outside cell tower coverage seems to be a display of “First World Hi-Tech Hubris”. If these drones were just intended to provide Internet (as Facebook was exploring), I would agree 100%.

However these drones can have cameras and other sensors to provide monitoring of the environment, climate change, and natural disasters that cell towers cannot. Given the benefits already provided by using Google Earth data for analysis of climate, population, infrastructure and more, one can easily see the doors that opened by feeding camera and sensor data from these drones to developers and researchers via Google’s Maps APIs (including weather and traffic layers and ‘satellite’ views).

Finally as these drones are powered by sunlight, they would continue to function and provide monitoring and Internet access even if a natural disaster took at power grids and energy pipelines for an area.

Satellite:

horizon-1One could easily argue that satellites (between Iridium, SPOT, INMARSAT, COMSAT, and all those government programs I cannot mention) cover all the gaps cellular technology misses. At 65,000’ of altitude, these drones would only be able to cover a 300-mile radius: satellites (depending on orbital parameters) can cover up to 160x this coverage area.

However, satellites are expensive (as we have learned with the disappearance of flight MH370), satellite is expensive (about $0.14-$0.18 per small 1-Kilobyte message). The reason for this high-cost is two-fold: the high-cost of launching a satellite and the distance they are above the earth (it takes over 1500x the power to transmit a signal to an Iridium satellite than it does to transmit a signal to a drone overhead at 65,000’).

This opens to door to communication with a whole new class of technologies, ones far less expensive than satphones. This includes everything from low-cost mobile phones to OLPC (One Laptop Per Child) laptops to sensors used to track endangered species and protect them against poaching.

This distance factor goes beyond power consumption to image resolution (Ground Sample Distance or GSD). Quite simply, a drone at 65,000’ can get photos with 6x the resolution of satellite in Low Earth Orbit (LEO) and 40x the resolution of satellites like SPOT.

A great addition, but not the only answer

The Google Drone concept is not a one-size-fits-all answer. It would take thousands of drones to cover the Earth, a very costly operation. While providing more coverage than cell towers, they would often be farther away and more costly to operate. While providing better bandwidth and GSD than satellite, they would have less coverage area. As such the answer, like all things in Internet access (and sensor technology) is a blended combination of fixed-line Internet, multiple terrestrial wireless technologies (from ZigBee to 4G), satellites and drones.

This begs an important question…

One question that has plagued me from the day I first saw Facebook’s interest in Titan was why communications companies like Vodafone (which is rather well known for its 21-country mobile SIM network) were not interested in companies like Titan. Overall, using drones for ubiquitous Internet would appear to be a much better strategic fit to a company that already charges customers for Internet access. Perhaps Google can make more money from higher-resolution image and sensor data than it would initially appear. Or perhaps these drones could serve as a potential grid network that could bypass carriers if the Net Neutrality wars go in a bad direction (just like Netflix is exploring with its peer-to-peer research).

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Only time will tell.