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Remy Guercio

Have you found the right APM?

11.17.2014 | Posted by Remy Guercio

When choosing an Application Performance Management (APM) provider to complement your log analytics solution, it is important to consider factors such as ease of integration, breadth of features offered and alignment with your growing business needs.  APM solutions are metrics focused, they do a great job of identifying latency spikes, drop offs in user interaction, and other metrics based issues. However, they can’t always identify the root cause of a problem or detect complex anomalies such as new error types in applications. Together, APM and log analytics solutions provide end-to-end visibility into your application stack. This post aims to provide some insight into the features and integrations that various APM providers offer with log analytics solutions. The APM vendors are listed in random order, not by any specific ranking.


New Relic

New Relic provides a SaaS based APM solution aimed at businesses with apps of all sizes. The APM solution provided by New Relic supports applications built on Ruby, Java, Node.js, PHP, Python, iOS and Android. The base APM solution integrates with their other solutions providing metrics based analytics from browsers, mobile platforms and synthetic testing monitors (in beta). New Relic was named as a leader in Gartner’s 2014 Magic Quadrant for APM.

Key Features:

  • Code level visibility
  • Transaction tracing
  • SQL query analysis
  • Alerting integrations with HipChat, Jira, PagerDuty and Campfire
  • Network request tracking
  • SLA compliance reporting
  • Mobile: Crash reporting
  • Mobile: Device analytics
  • Synthetic monitoring integration

Pricing Tiers:


  • 14 day free Pro trial
  • Lite – 24hr data retention
  • Pro – unlimited data retention, transaction tracing, phone support, and service SLA
  • Enterprise – dedicated account manager, greater support


  • Lite – 24hr data retention, summary data
  • Standard – 1 week data retention, response time metrics, user interaction overview
  • Enterprise – 3 month data retention, device metrics, user interaction traces

New Relic provides iPhone and Android applications where customers can view and receive alerts for important metrics, and on the web, New Relic offers a slick dashboard experience for looking into real-time application performance.



AppNeta provides a SaaS base APM solution that supports applications written in Java, .NET, Python, PHP, Node.js and Ruby. AppNeta was named as a niche player in Gartner’s 2014 Magic Quadrant for APM.

Key Features:

  • Synthetic monitoring
  • Code level visibility
  • Transaction tracing
  • SLA Compliance Reporting

Pricing Tiers:

TraceView (APM solution):

  • Free – 1 application, 1 hour of data retention, 1 user
  • Startup – 1 application, 24 hours of data retention, 3 users
  • Enterprise – unlimited applications, 45 days retention, unlimited users

AppView (Synthetic monitoring):

  • Small – 5 monitors
  • Medium – 10 monitors
  • Large – 40 monitors

AppNeta’s heat-map charting interface makes it very intuitive and easy to spot patterns, trends, and outliers in your apps metrics.



AppDynamics focuses on providing a SaaS application performance management solution to both large and small businesses, and they offer support for Java, .NET, PHP, Node.js, iOS and Android. They offer add ons that providing metrics based analytics from browsers and mobile platforms. Gartner named AppDynamics as a leader in the 2014 Magic Quadrant for APM.

Key Features:

  • Realtime user monitoring
  • Network request snapshots
  • Alerting integrations with Service Now, Pager Duty, and Jira
  • Code level visibility
  • Mobile: Crash reporting
  • Mobile: Device analytics
  • Synthetic monitoring

Pricing Tiers:

Note: Pricing is done in units. Units are usually 1 process each, except for with node.js where 10 processes equal 1 unit.


  • Lite – 24hr data retention, one unit
  • Pro – upto 10 units

AppDynamics’ dashboards allow for the creation of visually appealing and robust application component maps that show the detailed breakdown of your application’s performance in real time.

  … Continue Reading

Karthik Anantha Padmanabhan

Optimizing Breadth-First Search for Social Networks

10.28.2014 | Posted by Karthik Anantha Padmanabhan

Social network graphs, like the ones captured by Facebook and Twitter exhibit small-world characteristics [2][3]. In 2011, Facebook documented that among all Facebook users at the time of their research (721 million users with 69 billion friendship links), there is an average average shortest path distance of 4.74 between users. This simply means that on average, any two people in the world are separated by just five other people. It’s a small world indeed ! Formally, a small-world network is defined to be a network where the typical distance L between two randomly chosen nodes grows proportionally to the logarithm of the number of nodes N in the network [4].

Consider the following scenario. You have a social network profile and you want someone to introduce you to the person in that profile. But luckily you are given the entire friendship graph captured by this social network. If there are mutual friends, then you just ask one of them to help you out. If not, you need some sequence of friend introductions to finally meet that person. What is the minimum number of intermediate friend introductions that you need to meet that person you are interested in ? This is equivalent to finding the shortest path in the social network graph between you and that person of interest. The solution is to run Breadth First Search on that social network graph with your profile as the starting vertex. The other interesting question is, if we have extra information about our graph exhibiting small-world properties,  can we make the exhaustive Breadth-First Search (BFS) faster? The ideas expressed on this topic appeared in Beamer et al [1], where the authors optimized BFS for the number of edges traversed.

Breadth-First Search:

BFS uses the idea of a frontier that separates the visited nodes from unvisited nodes. The frontier holds the nodes of the recently visited level and is used to find the next set of nodes to be visited. On every step of BFS, the current frontier is used to identify the next frontier from the set of unvisited nodes.

Example Graph (1).png

Figure 1. A simple graph

Looking at the example in the figure, the current frontier consists of the nodes 1, 2, 3, 4 and 5. The edges from these nodes are examined to find a node that has not been visited. In the above case node 2’s edges are used to mark H and add it to the next frontier. But note that even though H has been marked by 2, nodes 3, 4 and 5 still inspect H to see whether it is visited or not.

Pseudocode for Naive BFS [5] :

Input: A graph G = (V,E) containing V vertices and E edges and source vertex s

Output: parent: Array[Int], where parent[v] gives the the parent of v in the graph or  -1 is if a parent does not exist

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
class BFS(g: Graph) {
val parent = ArrayBuffer.fill(g.numberVertices)(-1).toArray
def bfs(source: Int, updater: (Seq[Int], Array[Int]) => Seq[Int]) = {
var frontier = Seq(source)
parent(source) = -2
while (!frontier.isEmpty) {
frontier = updater(frontier, parent)
trait TopDownUpdater extends FrontierUpdater {
def update(frontier: Seq[Int], parents: Array[Int]): Seq[Int] = {
val next = ArrayBuffer[Int]()
frontier.foreach{ node =>
graph.getNeighbors(node).filter(parents(_) == -1).foreach { neighbor =>
next += neighbor
parents(neighbor) = node
view raw top-down.scala hosted with ❤ by GitHub

One of the observations of conventional BFS (henceforth referred to as top-down BFS) is that it always performs in the worst case complexity, i.e., O(|V| + |E|) where V and E are the number of vertices and number of edges respectively. For example, if a node v has p parents, then we just need to explore one edge from any p parents to v to check for connectivity. But top-down BFS checks all incoming edges to v.

The redundancy of these additional edge lookups is more pronounced when top-down BFS is run on graphs exhibiting small-world properties. As a consequence of the definition of small-world networks, the number of nodes increases exponentially with the effective diameter of the network, which result in large networks with very low diameters.  The low diameter of these graphs forces them to have a larger number of nodes at a particular level and leads to top-down BFS visiting a larger number of nodes in every step, making the frontier very large. Traversing the edges of the nodes in a frontier is the major computation that is performed, and top-down BFS unfortunately ends up visiting all the outgoing edges from the frontier. Moreover, it has also been shown in [1] that most of the edge lookups from the frontier nodes end up in visited nodes (marked by some other parent), which gives further evidence that iterating through all edges from the frontier can be avoided.

The idea behind bottom-up BFS [1] is to avoid visiting all the edges of the nodes in the frontier, which is a pretty useful thing to do for the reasons mentioned above. To accomplish this, bottom-up BFS traverses the edges of the unvisited nodes to find a parent in the current frontier. If an unvisited node has at least one of its parents in the current frontier, then that node is added to the next frontier. To efficiently find if a node’s parent is present in the frontier, the frontier data structure is changed to a bitmap.

Untitled drawing (4).pngUntitled drawing (6).png

Figure 2. Bottom up BFS

In the above example, {H, I, J, K } are the unvisited nodes. However only nodes { J, H } have a neighbor in the current frontier and as a result the next frontier now becomes {H , J}. In the next iteration the set of unvisited nodes will be {I, K} and each of them have a parent in the current frontier which is {H, J}. So {I, K} will be visited and the search will complete in the next iteration since there will be no more nodes to be added to the next frontier, since all nodes will be visited.


Pseudocode for Bottom-up BFS:

Input: A graph G = (V,E) containing V vertices and E edges and source vertex s

Output: parent: Array[Int], where parent[v] gives the the parent of v in the graph or  -1 is if a parent does not exist

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
trait DirectedSerialAncestorManager extends SerialAncestorManager{
var _graph: SerialDirectedGraph = _
def getAncestor(id: Int): IndexedSeq[Int] = {
def getVertices: IndexedSeq[Int] = (0 to _graph.numberVertices - 1)
trait SBottomUpUpdater extends FrontierUpdater with SerialAncestorManager {
def update(frontier: BitSet, parents: Array[Int]): Seq[Int] = {
val next = mutable.BitSet()
val vertices = getVertices
val frontierSet = frontier.toSet
(vertices.filter(parents(_) == -1)).foreach { node =>
val neighbors = (getAncestor(node))
neighbors.find(frontierSet) match {
case Some(ancestor) => {
parents(node) = ancestor
next(node) = true
case None => None
view raw bottom-up.scala hosted with ❤ by GitHub

 The major advantage to this approach is that the search for an unvisited node’s parent will terminate once any one parent is found in the current frontier. Contrast this with top-down BFS, which needs to visit all the neighbors of a node in the frontier during every step.

Top-down, Bottom-up, or both?

When the frontier is large, you gain by performing bottom-ups BFS as it only examines some edges of the unvisited nodes. But when the frontier is small, it may not be advantageous to perform bottom-up BFS, as apart from having to go over, it incurs the additional overhead of identifying the unvisited nodes. Small-world networks usually start off with small frontiers in the initial step and have an exponential increase in the frontier size in the middle stages of the search procedure. These tradeoffs lead us to another approach for small-world networks where we combine combine both top-down and bottom-up BFS—hybrid BFS  [1]. In hybrid BFS, the size of the frontier is used to define a heuristic, which is used to switch between the two approaches, top-down and bottom-up. A thorough analysis of this heuristic is presented in [1].

How about parallelizing these approaches ?  

When trying to parallelize the two approaches, observe that bottom-up BFS is easier to parallelize than top-down BFS. For bottom-up BFS, you can introduce parallelism in the stage where you populate the next frontier. Each of the unvisited nodes can be examined in parallel, and since every node just updates itself in the next data structure, it does not require the use of locks.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
trait ParallelAncestorManager {
def getAncestor(id: Int): ParSeq[Int]
def getParVertices: ParSeq[Int]
trait PBottomUpUpdater extends FrontierUpdater with ParallelAncestorManager {
def update(frontier: Seq[Int], parents: Array[Int]):Seq[Int] = {
val next = BitSet()
val frontierSet = frontier.toSet
getParVertices.filter(parents(_) == -1).foreach { node =>
val parNeighbors = getAncestor(node)
parNeighbors.find(x => frontierSet.contains(x)) match {
case Some(ancestor) => {
parents(node) = ancestor
next(node) = true
case None => None
view raw bottom-up-par.scala hosted with ❤ by GitHub

On inspecting the top-down BFS pseudo-code for sources of parallelism, observe that the nodes in the current frontier can be explored in parallel. The parallel top-down pseudo-code is:

1 2 3 4 5 6 7 8 9 10 11 12 13
trait PTopDownUpdater extends FrontierUpdater {
def update(frontier: Seq[Int], parents: Array[Int]): Seq[Int] = {
val next = ArrayBuffer[Int]()
frontier.par.foreach { node =>
graph.getNeighbors(node).filter(parents(_) == -1).foreach { neighbor =>
next += neighbor
parents(neighbor) = node
view raw top-down-par.scala hosted with ❤ by GitHub

 In terms of correctness, the above pseudo-code looks good, but there is a benign race condition introduced by updating parents and next. This may result in a node being added more than once, making it inefficient. But it does not affect the correctness of the algorithm. Cleaner code would have a synchronized block to ensure only one thread updates the frontier.

The hybrid approach combining the parallel versions of top-down and bottom-up BFS provides one of the fastest single node implementation of Parallel BFS [1].


  1. Beamer, Scott, Krste Asanović, and David Patterson. “Direction-optimizing breadth-first search.” Scientific Programming 21.3 (2013): 137-148.

  2. Ugander, Johan, et al. “The anatomy of the facebook social graph.” arXiv preprint arXiv:1111.4503 (2011).

  3. Li, Jun, Shuchao Ma, and Shuang Hong. “Recommendation on social network based on graph model.” Control Conference (CCC), 2012 31st Chinese. IEEE, 2012.

  4. Watts, Duncan J., and Steven H. Strogatz. “Collective dynamics of ‘small-world’networks.” nature 393.6684 (1998): 440-442.

  5. Introduction to Algorithms (1990) by T H Cormen, C E Leiserson, R L Rivest

Ariel Smoliar, Senior Product Manager

Transaction Mining for Deeper Machine Data Intelligence

10.22.2014 | Posted by Ariel Smoliar, Senior Product Manager

The new Sumo Logic Transaction capability allows users to analyze related sequences of machine data. The comprehensive views uncover user behavior, operational and security insights that can help organizations optimize business strategy, plans and processes.

The new capability allows you to monitor transactions by a specific transaction ID (session ID, IP, user name, email, etc.) while handling data from distributed systems, where a request is passed through several different systems, each with its own transaction ID.

Over the past two months, we have worked with beta customers on a variety of use cases, including:

  • Tracking transactions in a payment processing platform

  • Following typical user sessions, detecting anomalous checkout transactions and catching checkout drop off in e-commerce websites

  • Tracking renewals, upgrades and new signup transactions

  • Monitoring phone registrations failures over a specific period

  • Tracking on-boarding of new users in SaaS products

The last use case is reflective of what SaaS companies care most about: truly understanding the behavior of users on their website that drive long-term engagement. We’ve used our new transaction analytics capabilities to better understand how users find our site, the process by which they get to our Sumo Logic Free page, and how quickly they sign up. Our customer success team uses Transaction Analytics to monitor how long it takes users to create a dashboard, run a search, and perform other common actions. This enables them to provide very specific feedback to the product team for future improvements.

This screenshot depicts a query with IP as the transaction ID and the various states mapped from the logs

Sankey diagram visualizes the flow of the various components/states of a transaction on an e-commerce website

Many of our customers are already using tools such as Google Analytics to monitor visitors flow on their website and understand customer behavior. We are not launching this new capability to replace Google Analytics (even if it’s not embraced in some countries as Germany). What we bring on top of monitoring visitors flow, is the ability to identify divergence in state sequences and understand better the transitions between the states, in terms of latency for example. You probably see updates that some companies are announcing on plugins for log management platforms to detect anomalies and monitor user behavior and sessions. The team’s product philosophy is that we would like to provide our users all-rounded capability that enables them to make smart choices without requiring external tools, all from their machine data within the Sumo product.

It was a fascinating journey working on the transaction capability with our analytics team. It’s a natural evolution of our analytics strategy which now includes: 1) real-time aggregation and correlation with our Dashboards; 2) machine learning to automatically uncover anomalies and patterns; and 3) now transaction analytics to rapidly uncover relationships across distributed events.

We are all excited to launch Transaction Analytics. Please share with us your feedback on the new capability and let us know if we can help with your use cases. The transaction searches and the new visualization are definitely our favorite content.

Amanda Saso, Principal Tech Writer

Data, with a little Help from my friends

10.20.2014 | Posted by Amanda Saso, Principal Tech Writer


Ever had that sinking feeling when you start a new job and wonder just why you made the jump? I had a gut check when, shortly after joining Sumo Logic in June of 2012, I realized that we had less than 50 daily hits to our Knowledge Base on our support site. Coming from a position where I was used to over 7,000 customers reading my content each day, I nearly panicked.  After calming down, I realized that what I was actually looking at was an amazing opportunity.

Fast forward to 2014. I’ve already blogged about the work I’ve done with our team to bring new methods to deliver up-to-date content. (If you missed it, you can read the blog here.) Even with these improvements I couldn’t produce metrics that proved just how many customers and prospects we have clicking through our Help system. Since I work at a data analytics company, it was kind of embarrassing to admit that I had no clue how many visitors were putting their eyes on our Help content. I mean, this is some basic stuff!

Considering how much time I’ve spent working with our product, I knew that I could get all the information I needed using Sumo Logic…if I could get my hands on some log data. I had no idea how to get logging enabled, not to mention how logs should be uploaded to our Service. Frankly, my English degree is not conducive to solving engineering challenges (although I could write a pretty awesome poem about my frustrations). I’m at the mercy of my Sumo Logic co-workers to drive any processes involving how Help is delivered and how logs are sent to Sumo Logic.  All I could do was pitch my ideas and cross my fingers.

I am very lucky to work with a great group of people who are happy to help me out when they can. This is especially true of Stefan Zier, our Chief Architect, who once again came to my aid. He decommissioned old Help pages (my apologies to anyone who found their old bookmarks rudely displaying 404’s) and then routed my Help from the S3 bucket through our product, meaning that Help activity can be logged. I now refer to him as Stefan, Patron Saint of Technical Writers. Another trusty co-worker we call Panda helped me actually enable the logging.

Once the logging began we could finally start creating some Monitors to build out a Help Metrics Dashboard. In addition to getting the number of hits and the number of distinct users, we really wanted to know which pages were generating the most hits (no surprise that search-related topics bubbled right to the top). We’re still working on other metrics, but let me share just a few data points with you.


Take a look at the number of hits our Help site has handled since October 1st:


We now know that Wednesday is when you look at Help topics the most:


And here’s where our customers are using Help, per our geo lookup operator Monitor:


It’s very exciting to see how much Sumo Logic has grown, and how many people now look at content written by our team, from every corner of the world. Personally, it’s gratifying to feel a sense of ownership over a dataset in Sumo Logic, thanks to my friends.

What’s next from our brave duo of tech writers? Beyond adding additional logging, we’re working to find a way to get feedback on Help topics directly from users. If you have any ideas or feedback, in the short term, please shoot us an email at We would love to hear from you!

Kumar Saurabh, Co-Founder & VP of Engineering

Machine Data Intelligence – an update on our journey

10.16.2014 | Posted by Kumar Saurabh, Co-Founder & VP of Engineering

In 1965, Dr. Hubert Dreyfus, a professor of philosophy at MIT, later at Berkeley, was hired by RAND Corporation to explore the issue of artificial intelligence.  He wrote a 90-page paper called “Alchemy and Artificial Intelligence” (later expanded into the book What Computers Can’t Do) questioning the computer’s ability to serve as a model for the human brain.  He also asserted that no computer program could defeat even a 10-year-old child at chess.

Two years later, in 1967, several MIT students and professors challenged Dreyfus to play a game of chess against MacHack (a chess program that ran on a PDP-6 computer with only 16K of memory).  Dreyfus accepted. Dreyfus found a move, which could have captured the enemy queen.  The only way the computer could get out of this was to keep Dreyfus in checks with his own queen until he could fork the queen and king, and then exchange them.  And that’s what the computer did.  The computer checkmated Dreyfus in the middle of the board.

I’ve brought up this “man vs. machine” story because I see another domain where a similar change is underway: the field of Machine Data.

Businesses run on IT and IT infrastructure is getting bigger by the day, yet IT operations still remain very dependent on analytics tools with very basic monitoring logic. As the systems become more complex (and more agile) simple monitoring just doesn’t cut it. We cannot support or sustain the necessary speed and agility unless the tools becomes much more intelligent.

We believed in this when we started Sumo Logic and with the learnings of running a large-scale system ourselves, continue to invest in making operational tooling more intelligent. We knew the market needed a system that complemented the human expertise. Humans don’t scale that well – our memory is imperfect so the ideal tools should pick up on signals that humans cannot, and at a scale that perfectly matches the business needs and today’s scale of IT data exhaust.

Two years ago we launched our service with a pattern recognition technology called LogReduce and about five months ago we launched Structure Based Anomaly Detection. And the last three months of the journey have been a lot like teaching a chess program new tricks – the game remains the same, just that the system keeps getting better at it and more versatile.

We are now extending our Structured Based Anomaly Detection capabilities with Metric Based Anomaly Detection. A metric could be just that – a time series of numerical value. You can take any log, filter, aggregate and pre-process however you want – and if you can turn that into a number with a time stamp – we can baseline it, and automatically alert you when the current value of the metric goes outside an expected range based on the history. We developed this new engine in collaboration with the Microsoft Azure Machine Learning team, and they have some really compelling models to detect anomalies in a time series of metric data – you can read more about that here.

The hard part about Anomaly Detection is not about detecting anomalies – it is about detecting anomalies that are actionable. Making an anomaly actionable begins with making it understandable. Once an analyst or an operator can grok the anomalies – they are much more amenable to alert on it, build a playbook around it, or even hook up automated remediation to the alert – the Holy Grail.

And, not all Anomaly Detection engines are equal. Like chess programs there are ones that can beat a 5 year old and others that can even beat the grandmasters. And we are well on our way to building a comprehensive Anomaly Detection engine that becomes a critical tool in every operations team’s arsenal. The key question to ask is: does the engine tell you something that is insightful, actionable and that you could not have found with standard monitoring tools.

Below is an example of  an actual Sumo production use case where some of our nodes were spending a lot of time in garbage collection impacting refresh rates for our dashboards for some of the customers.


If this looks interesting, our Metric Based Anomaly Detection service based on Azure Machine Learning is being offered to select customers in a limited beta release and will be coming soon to machines…err..a browser near you (we are a cloud based service after all).

P.S. If you like stories, here is another one for you. 30 years after MackHack beat Dreyfus, in the year 1997  Kasparov (arguably one of the best human chess players) played the Caro-Kann Defence. He then allowed Deep Blue to commit a knight sacrifice, which wrecked his defenses and forced him to resign in fewer than twenty moves.  Enough said.






David Andrzejewski, Data Sciences Engineer

Scala at Sumo: type class law for reliable abstractions

10.09.2014 | Posted by David Andrzejewski, Data Sciences Engineer

Abstraction is a fundamental concept in software development. Identifying and building abstractions well-suited to the problem at hand can make the difference between clear, maintainable code and a teetering, Jenga-like monolith duct-taped together by a grotesque ballet of tight coupling and special case handling. While a well-designed abstraction can shield us from detail, it can also suffer from leakage, failing to behave as expected or specified and causing problems for code built on top of it. Ensuring the reliability of our abstractions is therefore of paramount concern.

In previous blog posts, we’ve separately discussed the benefits of using type classes in Scala to model abstractions, and using randomized property testing in Scala to improve tests. In this post we discuss how to combine these ideas in order to build more reliable abstractions for use in your code. If you find these ideas interesting please be sure to check out the references at the end of this post.

Type classes for fun and profit

Type classes allow us to easily build additional behaviors around data types in a type-safe way. One simple and useful example is associated with the monoid abstraction, which represents a set of items which can be combined with one another (such as the integers, which can be combined by addition). Loosely[1], a monoid consists of

  • a collection of objects (e.g., integers)

  • a binary operation for combining these objects to yield a new object of the same type (e.g., addition)

  • an identity object whose combination leaves an object unchanged (e.g., the number 0)

This abstraction is captured by the scalaz trait Monoid[F]:

1 2 3 4
trait Monoid[F] {
def zero: F
def append(f1: F, f2: => F): F
view raw lawblog-monoid.scala hosted with ❤ by GitHub

The utility of this machinery is that it gives us a generalized way to use types that support some notion of “addition” or “combination”, for example[2]:

1 2 3 4 5 6 7 8 9 10
def addItUp[F : Monoid](items: Seq[F]): F = {
// Combine a bunch of items
val m = implicitly[Monoid[F]]
items.foldLeft({case (total, next) => m.append(total,next)}
scala> addItUp(Seq("day ", "after ", "day"))
res1: String = "day after day"
scala> addItUp(Seq(1,2,3))
res2: Int = 6

As described in our earlier machine learning example, this can be more convenient than requiring that the data types themselves subtype or inherit from some kind of “Addable” interface.

I am the law!

In Scala, the Monoid[F] trait definition (combined with the compiler type-checking) buys us some important sanity checks with respect to behavior. For example, the function signature append(x: F, y: F): F guarantees that we’re never going to get a non-F result[3].

However, there are additional properties that an implementation of Monoid[F] must satisfy in order to truly conform to the conceptual definition of a monoid, but which are not easily encoded into the type system. For example, the monoid binary operation must satisfy left and right identity with respect to the “zero” element. For integers under addition the zero element is 0, and we do indeed have x + 0 = 0 + x = x for any integer x.

We can codify this requirement in something called type class law. When defining a particular type class, we can add some formal properties or invariants which we expect implementations to obey. The codification of these constraints can then be kept alongside the type class definition. Again returning to scalaz Monoid[4], we have

1 2 3 4 5 6 7 8 9 10
trait Monoid[F] extends Semigroup[F] {
trait MonoidLaw extends SemigroupLaw {
def leftIdentity(a: F)(implicit F: Equal[F]) =
F.equal(a, append(zero, a))
def rightIdentity(a: F)(implicit F: Equal[F]) =
F.equal(a, append(a, zero))

An interesting observation is that this implementation depends upon another type class instance Equal[F] which simply supplies an equal() function for determining whether two instances of F are indeed equal. Of course, Equal[F] comes supplied with its own type class laws for properties any well-defined notion of equality must satisfy such as commutativity (x==y iff y==x), reflexivity (x==x), and transitivity (if a==b and b==c then a==c).

A machine learning example

We now consider an example machine learning application where we are evaluating some binary classifier (like a decision tree) over test data. We run our evaluation over different sets of data, and for each set we produce a very simple output indicating how many predictions were made, and of those, how many were correct: 

case class Evaluation(total: Int, correct: Int)

We can implement Monoid[Evaluation] [5] in order to combine the our experimental results across multiple datasets:

1 2 3 4 5
object EvaluationMonoid extends Monoid[Evaluation] {
def zero = Evaluation(0,0)
def append(x: Evaluation, y: => Evaluation) =
Evaluation( +, x.correct + y.correct)

We’d like to ensure that our implementation satisfies the relevant type class laws. We could write a handful of unit tests against one or more hand-coded examples, for example using ScalaTest:

1 2 3 4 5 6 7 8 9 10 11
"Evaluation Monoid" should {
import EvaluationMonoid._
implicit val eq = Equal.equalA[Evaluation]
val testEvaluation = Evaluation(3, 2)
"obey Monoid typeclass Law" in {
Monoid.monoidLaw.leftIdentity(testEval) should be (true)
Monoid.monoidLaw.rightIdentity(testEval) should be (true)

However, this merely gives us an existence result. That is, there exists some value for which our the desired property holds. We’d like something a little stronger. This is where we can use ScalaCheck to do property testing, randomly generating as many arbitrary instances of Evaluation as we’d like. If the law holds for all [6] generated instances, we can have a higher degree confidence in the correctness of our implementation. To accomplish this we simply need to supply a means of generating random Evaluation instances via ScalaCheck Gen:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
val evalGen = for {total <- Gen.choose(0, 1000);
correct <- Gen.choose(0, total)}
yield Evaluation(total,correct)
"Evaluation Monoid" should {
import EvaluationMonoid._
implicit val eq = Equal.equalA[Evaluation]
"obey Monoid typeclass Law" in {
forAll (evalGen) { testEval => {
Monoid.monoidLaw.leftIdentity(testEval) should be (true)
Monoid.monoidLaw.rightIdentity(testEval) should be (true)

Now that’s an abstraction we can believe in!

So what?

This level of confidence becomes important when we begin to compose type class instances, mixing and matching this machinery to achieve our desired effects. Returning to our Evaluation example, we may want to evaluate different models over these datasets, storing the results for each dataset in a Map[String,Evaluation] where the keys refer to which model was used to obtain the results. In scalaz, we get the Monoid[Map[String,Evaluation]] instance “for free”, given an instance of Monoid[Evaluation]:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
scala> implicit val em = EvaluationMonoid
em: EvaluationMonoid.type = EvaluationMonoid$@34f5b235
scala> implicit val mm = mapMonoid[String,Evaluation]
mm: scalaz.Monoid[Map[String,Evaluation]] = scalaz.std.MapInstances$$anon$4@13105b09
scala> val dataset1 = Map("modelA" -> Evaluation(3,2),
| "modelB" -> Evaluation(4,1))
dataset1: scala.collection.immutable.Map[String,Evaluation] =
Map(modelA -> Evaluation(3,2), modelB -> Evaluation(4,1))
scala> val dataset2 = Map("modelA" -> Evaluation(5,4))
dataset2: scala.collection.immutable.Map[String,Evaluation] =
Map(modelA -> Evaluation(5,4))
scala> mm.append(dataset1,dataset2)
res3: Map[String,Evaluation] =
Map(modelA -> Evaluation(8,6), modelB -> Evaluation(4,1))

Conclusion and references

If you are using the scalaz library, many of the provided type classes come “batteries included” with type class laws. Even if you are not, these ideas can help you to build more reliable type class instances which can be composed and extended with confidence. See below for some additional references and readings on this subject:


[1] Omitting associativity and explicit discussion of closure.

[2] For brevity, these code snippets do not show library (scalaz, ScalaTest, ScalaCheck) imports.

[3] Excluding the unfortunate possibilities of null return values or thrown Exceptions.

[4] A semigroup is a more general concept than a monoid, which is modeled in scalaz by having Monoid[F] extend Semigroup[F].

[5] This implementation has a bit of a boilerplate flavor, this post describes how we could automagically derive our Monoid[Evaluation] instance.

[6] As implied by the ScalaCheck project’s appropriate logo.

Cloud Log Management for Control Freaks

10.02.2014 | Posted by Bright Fulton

The following is a guest post from Bright Fulton, Director of Engineering Operations at Swipely.

Like other teams that value their time and focus, Swipely Engineering strongly prefers partnering with third party infrastructure, platform, and monitoring services. We don’t, however, like to be externally blocked while debugging an issue or asking a new question of our data. Is giving up control the price of convenience? It shouldn’t be. The best services do the heavy lifting for you while preserving flexibility. The key lies in how you interface with the service: stay in control of data ingest and code extensibility.

A great example of this principle is Swipely’s log management architecture. We’ve been happily using Sumo Logic for years. They have an awesome product and are responsive to their customers. That’s a strong foundation, but because logging is such a vital function, we retain essential controls while taking advantage of all the power that Sumo Logic provides.

Get the benefits

Infrastructure services have flipped our notion of stability: instead of being comforted by long uptime, we now see it as a liability. Instances start, do work for an hour, terminate. But where do the logs go? One key benefit of a well integrated log management solution is centralization: stream log data off transient systems and into a centralized service.

Once stored and indexed, we want to be able to ask questions of our logs, to react to them. Quick answers come from ad-hoc searches:

  • How many times did we see this exception yesterday?

  • Show me everything related to this request ID.

Next, we define scheduled reports to catch issues earlier and shift toward a strategic view of our event data.

  • Alert me if we didn’t process a heartbeat job last hour.

  • Send me a weekly report of which instance types have the worst clock skew.

Good cloud log management solutions make this centralization, searching, and reporting easy.

Control the data

It’s possible to get these benefits without sacrificing control of the data by keeping the ingest path simple: push data through a single transport agent and keep your own copy. Swipely’s logging architecture collects with rsyslog and processes with Logstash before forwarding everything to both S3 and Sumo Logic.

Swipely’s Logging Architecture

Put all your events in one agent and watch that agent.

You likely have several services that you want to push time series data to: logs, metrics, alerts. To solve each concern independently could leave you with multiple long running agent processes that you need to install, configure, and keep running on every system. Each of those agents will solve similar problems of encryption, authorization, batching, local buffering, back-off, updates. Each comes with its own idiosyncrasies and dependencies. That’s a lot of complexity to manage in every instance.

The lowest common denominator of these time series event domains is the log. Simplify by standardizing on one log forwarding agent in your base image. Use something reliable, widely deployed, open source. Swipely uses rsyslog, but more important than which one is that there is just one.

Tee time

It seems an obvious point, but control freaks shouldn’t need to export their data from third parties. Instead of forwarding straight to the external service, send logs to an aggregation server first. Swipely uses Logstash to receive the many rsyslog streams. In addition to addressing vendor integrations in one place, this point of centralization allows you to:

  • Tee your event stream. Different downstream services have different strengths. Swipely sends all logs to both Sumo Logic for search and reporting and to S3 for retention and batch jobs.

  • Apply real-time policies. Since Logstash sees every log almost immediately, it’s a great place to enforce invariants, augment events, and make routing decisions. For example, logs that come in without required fields are flagged (or dropped). We add classification tags based on source and content patterns. Metrics are sent to a metric service. Critical events are pushed to an SNS topic.

Control the code

The output is as important as the input. Now that you’re pushing all your logs to a log management service and interacting happily through search and reports, extend the service by making use of indexes and aggregation operators from your own code.

Wrap the API

Good log management services have good APIs and Sumo Logic has several. The Search Job API is particularly powerful, giving access to streaming results in the same way we’re used to in their search UI.

Swipely created the sumo-search gem in order to take advantage of the Search Job API. We use it to permit arbitrary action on the results of a search.

Custom alerts and dashboards

Bringing searches into the comfort of the Unix shell is part of the appeal of a tool like this, but even more compelling is bringing them into code. For example, Swipely uses sumo-search from a periodic job to send alerts that are more actionable than just the search query results. We can select the most pertinent parts of the message and link in information from other sources. 

Engineers at Swipely start weekly tactical meetings by reporting trailing seven day metrics. For example: features shipped, slowest requests, error rates, analytics pipeline durations. These indicators help guide and prioritize discussion. Although many of these metrics are from different sources, we like to see them together in one dashboard. With sumo-search and the Search Job API, we can turn any number from a log query into a dashboard widget in a couple lines of Ruby.

Giving up control is not the price of SaaS convenience. Sumo Logic does the heavy lifting of log management for Swipely and provides an interface that allows us to stay flexible. We control data on the way in by preferring open source tools in the early stages of our log pipeline and saving everything we send to S3. We preserve our ability to extend functionality by making their powerful search API easy to use from both shell and Ruby.

We’d appreciate feedback (@swipelyeng) on our logging architecture. Also, we’re not really control freaks and would love pull requests and suggestions on sumo-search!

Vivek Kaushal

Debugging Amazon SES message delivery using Sumo Logic

10.02.2014 | Posted by Vivek Kaushal


We at Sumo Logic use Amazon SES (Simple Email Service) for sending thousands of emails every day for things like search results, alerts, account notifications etc. We need to monitor SES to ensure timely delivery and know when emails bounce.

Amazon SES provides notifications about status of email via Amazon SNS (Simple Notification Service). Amazon SNS allows you to send these notifications to any HTTP endpoint. We ingest these messages using Sumo Logic’s HTTP Source.

Using these logs, we have identified problems like scheduled searches which always send results to an invalid email address; and a Microsoft Office 365 outage when a customer reported having not received the sign up email.


Here’s a step by step guide on how to send your Amazon SES notifications to Sumo Logic.

1. Set Up Collector. The first step is to set up a hosted collector in Sumo Logic which can receive logs via HTTP endpoint. While setting up the hosted collector, we recommend providing an informative source category name, like “aws-ses”.  

2. Add HTTP Source. After adding a hosted collector, you need to add a HTTP Source. Once a HTTP Source is added, it will generate a URL which will be used to receive notifications from SNS. The URL looks like  

3. Create SNS Topic. In order to send notifications from SES to SNS, we need to create a SNS topic. The following picture shows how to create a new SNS topic on the SNS console. We uses “SES-Notifications” as the name of the topic in our example.

4. Create SNS Subscription. SNS allows you to send a notification to multiple HTTP Endpoints by creating multiple subscriptions within a topic. In this step we will create one subscription for the SES-Notifications topic created in step 3 and send notifications to the HTTP endpoint generated in step 2.

5. Confirm Subscription. After a subscription is created, Amazon SNS will send a subscription confirmation message to the endpoint. This subscription confirmation notification can be found in Sumo Logic by searching for: _sourceCategory=<name of the sourceCategory provided in step 1>

For example: _sourceCategory=aws-ses 

Copy the link from the logs and paste it in your browser.

6. Send SES notifications to SNS. Finally configure SES to send notifications to SNS. For this, go to the SES console and select the option of verified senders on the left hand side. In the list of verified email addresses, select the email address for which you want to configure the logs. The page looks like

On the above page, expand the notifications section and click edit notifications. Select the SNS topic you created in step 3.


7. Switch message format to raw (Optional). SES sends notifications to SNS in a JSON format. Any notification sent through SNS is by default wrapped into a JSON message. Thus in this case, it creates a nested JSON, resulting in a nearly unreadable message. To remove this problem of nested JSON messages, we highly recommend configuring SNS to use raw message delivery option.

Before setting raw message format

After setting raw message format



JSON operator was used to easily parse the messages as show in the queries below:

1. Retrieve general information out of messages
_sourceCategory=aws-ses | json “notificationType”, “mail”, “mail.destination”, “mail.destination[0]“, “bounce”, “bounce.bounceType”, “bounce.bounceSubType”, “bounce.bouncedRecipients[0]” nodrop

2. Identify most frequently bounced recipients
_sourceCategory=aws-ses AND !”notificationType\”:\”Delivery” | json “notificationType”, “mail.destination[0]” as type,destination  nodrop | count by destination | sort by _count

Vera Chen

We are Shellshock Bash Bug Free Here at Sumo Logic, but What about You?

10.01.2014 | Posted by Vera Chen

Be Aware and Be Prepared

I am betting most of you have heard about the recent “Shellshock Bash Bug”.  If not, here is why you should care – this bug has affected users of Bash, which is one of the most popular utilities installed on operating systems today.  Discovered in early September 2014, this extremely severe bug affects bash versions dating back to version 1.13 and has the ability to process shell commands after function definitions in Bash that exposes systems to security threats.  This vulnerability allows remote attackers to execute any shell command and gain access to internal data, publish malicious code, reconfigure environments and exploit systems in infinite ways.

Shellshock Bash Bug Free, Safe and Secure

None of the Sumo Logic service components were impacted due to the innate design of our systems.  However, for those of you out there who might have fallen victim to this bug based on your system architecture, you’ll want to jump in quickly to address potential vulnerabilities. 

What We Can Do for You

If you have been searching around for a tool to expedite the process of identifying potential attacks on your systems, you’re in the right place!  I recommend that you consider Sumo Logic and especially our pattern recognition capability called LogReduce.  Here is how it works – the search feature enables you to search for the well known “() {“ Shellshock indicators while the touch of the LogReduce button effectively returns potential malicious activity for you to consider.  Take for instance a large group of messages that could be a typical series of ping requests, LogReduce separates messages by their distinct signatures making it easier for you to review those that differ from the norm.  You can easily see instances of scans, attempts and real attacks separated into distinct groups.  This feature streamlines your investigation process to uncover abnormalities and potential attacks.  Give it a try and see for yourself how LogReduce can reveal to you a broad range of remote attacker activity from downloads of malicious files to your systems, to internal file dumps for external retrieval, and many others.

Witness it Yourself

Check out this video to see how our service enables you to proactively identify suspicious or malicious activity on your systems: Sumo Logic: Finding Shellshock Vulnerabilities

Give Us a Try

For those of you, who are completely new to our service, you can sign up for a Free 30 day trail here: Sumo Logic Free 30 Day Trial


LogReduce vs Shellshock

09.25.2014 | Posted by Joan Pepin, VP of Security/CISO


Shellshock is the latest catastrophic vulnerability to hit the Internet. Following so closely on the heels of Heartbleed, it serves as a reminder of how dynamic information security can be.

(NOTE: Sumo Logic was not and is not vulnerable to this attack, and all of our BASH binaries were patched on Wednesday night.)


Right now there is a massive land-grab going on, as dozens of criminal hacker groups (and others) are looking to exploit this widespread and serious vulnerability for profit. Patching this vulnerability while simultaneously sifting through massive volumes of data looking for signs of compromise is a daunting task for your security and operations teams. However, Sumo Logic’s patent pending LogReduce technology can make this task much easier, as we demonstrated this morning.

Way Big Data

While working with a customer to develop a query to show possible exploitation of Shellshock, we saw over 10,000 exploitation attempts in a fifteen minute window. It quickly became clear that a majority of the attempts were being made by their internal scanner. By employing LogReduce we were able to very quickly pick out the actual attack attempts from the data-stream, which allowed our customer to focus their resources on the boxes that had been attacked.


Fighting the Hydra

From a technical perspective, the Shellshock attack can be hidden in any HTTP header; we have seen it in the User-Agent, the Referrer, and as part of the GET request itself. Once invoked in this way, it can be used to do anything from sending a ping, to sending an email, to installing a trojan horse or opening a remote shell. All of which we have seen already today. And HTTP isn’t even the only vector, but rather just one of many which may be used, including DHCP.

So- Shellshock presents a highly flexible attack vector and can be employed in a number of ways to do a large variety of malicious things. It is is so flexible, there is no single way to search for it or alert on it that will be completely reliable. So there is no single silver bullet to slay this monster, however, LogReduce can quickly shine light on the situation and wither it down to a much less monstrous scale.

We are currently seeing many different varieties of scanning, both innocent and not-so-innocent; as well as a wide variety of malicious behavior, from directly installing trojan malware to opening remote shells for attackers. This vulnerability is actively being exploited in the wild this very second. The Sumo Logic LogReduce functionality can help you mitigate the threat immediately.