Social Security Company Nexgate's Success Relies on Apache Cassandra


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The accuracy of any security product is directly tied to the breadth of the corpus of data upon which it is built. For Nexgate, this means that the success of our products is inextricably tied to our ability to save everything we've ever scanned forever, but in a way that is still readily accessible. In the days before NoSQL, this was hard. This is how Datastax and Cassandra make it easy

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  • Understanding and managing the touch points and scale of your social presenceLow barrier to adoption => unmanaged account sprawlFocus on sentiment alone => miss activity, risks, and opportunities on what your company is responsible forManual moderation/measurement => Rapidly rising costs, reduced effectiveness, risks of PR crisesEstablishing governance policies and processes to protect your brandSocial accounts & applications live outside the corporate network => corporate governance and security risksSiloed account owners => no auditing of account accessManual moderation for content on accounts => higher probability for errors and crises
  • Social Security Company Nexgate's Success Relies on Apache Cassandra

    1. 1. Datastax and Cassandra at Nexgate Rich Sutton, CTO Harold Nguyen, Sr. Data Scientist
    2. 2. A Little About Us Company – Security & Compliance for Social  Launched April 2013 - Series A from Sierra & WindForce Ventures – 15 employees, 7 in Engineering (2 Data Scientists)  Security guys from:  Customers:
    3. 3. Key Enterprise Pain Points ① Brand social account sprawl • Can‟t inventory, audit, track social media infrastructure • Can‟t continuously find fake accounts ② Inbound protection for accounts • Nothing to detect and remediate account anomalies / hacks • No automated coverage for volumes of inappropriate and malicious content ③ Outbound compliance controls • Too many admins and apps installed across multiple accounts • Little or no automated coverage for sensitive and regulated data Novartis Slapped by the FDA FINRA begins social compliance audits Spam
    4. 4. Where Nexgate Fits Protecting the social account itself Nexgate Protect branded accounts and ensure compliance  Find, audit, and track the actual social accounts of the brand  Catch & remediate social account hacks, tampering, and misuse  Remove bad „inbound‟ content including spam, malware, and acceptable use  Enforce usage of approved publishing platforms  Comply with regulations using prebuilt content policies, workflow, and intelligent archiving Listening Platforms Mine external social data and conversations • Find brand „mentions‟ and present them with inferences • Provide volumes of market data that is analyzed for trends, share of voice, etc. • Social CRM identification of key conversations and influencers that may need engagement Publishing Platforms Engage audiences and track outcomes • Build communities • Deliver content, custom apps, ads with workflow • Promotions, contests, and campaigns
    5. 5. :001> Content classification is what we do. The completeness of any classification system is predicated on the breadth of the corpus of data upon which it is built.
    6. 6. :002> We made a lazy storage choice.
    7. 7. :003> Some success forced our hand.
    8. 8. :004> Social data is small and jagged. • Average 1K all in, content and metadata • Some common small stuff: time, social IDs, parent, account • Some common big stuff: content, links • Lots of disparate stuff, specific to the social platform
    9. 9. :005> Keep in SQL: Fixed length, non-null, heavily indexed, group access Keep in NoSQL: Variable length, commonly null, non indexed, single access, text search
    10. 10. :006> Requirements • Simple, proven horizontal scalability • Integrated tools for research: search, analysis • Operational simplicity; nodes all the same • Enterprise support
    11. 11. :007> Deployment • Multi-region AWS • M1 Large instances • Instance attached storage • About to scale again • Separate dev, test, prod clusters Datastax: • Start-up pricing, per-core pricing • On site experts, responsive support
    12. 12.  Over 250 million pieces of social media total content spread across Facebook, Twitter, YouTube, Google+, LinkedIn  Currently about half a million new content per day – All classified in real time as it comes in  About 50,000 new social media content authors per day  Cassandra is a great choice for a database– allows flexibility for the ever rapidly-changing landscape of social media threats Scale of Data
    13. 13. Data throughput Average reads = 70 / sec Average writes = 25 / sec
    14. 14.  Among the many security and compliance classifications that Nexgate provides, we also have powerful spam detection  Spam can be a single link directing to a fraudulent site (screenshots of a Facebook comment): Fighting Spam with Cassandra
    15. 15.  Or it can be less obvious, and more personal. This is extremely common. Here, the same user has posted the same message across different social media accounts (screenshot taken from Nexgate product):
    16. 16. Social media spam grew by 355% in the first half of 2013. Get the report at
    17. 17.  Can create Spam signatures to catch this type of content  ...but it would be too slow to catch Spam in real time.  Cassandra Cassandra and Social Media Spam
    18. 18.  Even though Cassandra is a NoSQL schema- less database, it is worth carefully defining the data model  Can‟t just “throw data at it” – can make for some really inefficient queries  Define the data model based on how you will query the data  For us, we want to determine spam content that has been posted duplicate times – Spammers tend to post same-content messages Define Your Data Model
    19. 19.  Typical table in Cassandra – Wide “unconstrained” rows is a nice feature w.r.t. SQL Spam Multiplicity Data Model  Row key -> hash of content  Column Key -> Unique ID (strictly increasing with time)  Column Value -> Item_id and time of post
    20. 20.  Spammers typically post the same content over and over  Easy to determine how many times a same-content post is made: check the number of columns  Will never double count because the column key will simply be updated instead of added  Indexed by the content, so quick reads and writes  By reading the column value, can extract the time series information of duplicated posts – Can also map back to the original value – we store actual content indexed by the item_id in another Cassandra table  Cassandra not a magic bullet – still need a relational database to glue all the pieces of data together – Batch processing may need other tools like Hadoop Why this Data Model ?
    21. 21.  This has become invaluable to us for catching spam content in real time – the following “rant” comment was posted 38 times… – Brand can more easily moderate given automated tools Real-world spam multiplicity  In another example, a customer received 25,000 inappropriate messages, and this tool helped us automate content removal
    22. 22.  Another way to tackle real-time spam is by identifying spammy users – Since Cassandra effortlessly keeps all the content we observed, our algorithm takes into account all the posts contributed by an author to determine if they are a spammer  Additionally, it is important to keep all data to train our 100+ classifiers Importance of Keeping All Data
    23. 23.  Cassandra actually has been humming along quite nicely! – Barely any tweaking needed from default values – No deletes (just the nature of our dataset) => not a lot of frequent repairs performed (repair is done to resolve inconsistencies across all replicas of data due to deletes) • Fine for us, because repair requires intensive disk I/O  Only times we observed performance issues: – When the rates of our reads and writes reached a certain threshold – When the size of the data being inserted was too large – Heap memory issue with Cassandra 1.1.x  In all cases, Datastax provided a quick and simple solution, mostly just toggling a few parameters in config files and restarting the nodes Tuning Cassandra
    24. 24.  Community is wonderful - it's really easy to jump on the Cassandra IRC channel and talk to fellow users and developers to get real-time feedback. – With IRC and mailing list help, implemented composite columns to detect malware sites on the second day of using Cassandra 3 years ago  In fact, when we tested a migration to the latest version of Casandra, and one of our Ruby wrappers didn't play nice with CQL3, I was able to speak directly with the Ruby wrapper author on IRC and received a reason on why it didn't work. – In the same day, I committed and made a pull request for a fix to the Ruby wrapper on github, and the author looked at it the next morning  Datastax support has been invaluable for providing fast feedback and simple solutions Cassandra Community
    25. 25.  OpsCenter helpful in debugging performance issues  Solr – used to obtain training data for classifiers by phrase matching  Looking forward: – Datastax Hadoop support to look into training labeled data with MapReduce Datastax Additional Tools
    26. 26. Thank you Datastax and RelateIQ! Let us show you: Follow us: @NXGate