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Using Apache Pulsar to Provide Real-Time IoT Analytics on the Edge

September 09, 2019

Using Apache Pulsar to Provide Real-Time IoT Analytics on the Edge

The business value of data decreases rapidly after it is created, particularly in use cases such as fraud prevention, cybersecurity, and real-time system monitoring. The high-volume, high-velocity datasets used to feed these use cases often contain valuable, but perishable, insights that must be acted upon immediately. In order to maximize the value of their data enterprises must fundamentally change their approach to processing real-time data to focusing reducing their decision latency on the perishable insights that exist within their real-time data streams. Thereby enabling the organization to act upon them while the window of opportunity is open. Generating timely insights in a high-volume, high-velocity data environment is challenging for a multitude of reasons. As the volume of data increases, so does the amount of time required to transmit it back to the datacenter and process it. Secondly, as the velocity of the data increases, the faster the data and the insights derived from it lose value. In this talk, we will present a solution based on Apache Pulsar Functions that significantly reduces decision latency by using probabilistic algorithms to perform analytic calculations on the edge.


September 09, 2019

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  1. Apache Pulsar • Cloud Native Messaging Platform developed at Yahoo!

    • Horizontally Scalable – Topics, Storage • Provides message ordering, durability, and delivery guarantees • Supports both Queuing and Pub/Sub messaging. • Decoupled Serving and Storage Layers allows for edge deployment
  2. Defining IoT Analytics • It is NOT JUST loading sensor

    data into a data lake to create predictive analytic models. While this is crucial piece of the puzzle, it is not the only one. • IoT Analytics requires the ability to ingest, aggregate, and process an endless stream of real-time data coming off a wide variety of sensor devices “at the edge” • IoT Analytics renders real-time decisions at the edge of the network to either optimize operational performance or detect anomalies for immediate remediation. 3
  3. IoT Analytics Challenges • IoT deals with machine generated data

    consisting of discrete observations such as temperature, vibration, pressure, etc. that is produced at very high rates. • We need an architecture that: • Allows us to quickly identify and react to anomalous events • Reduces the volume of data transmitted back to the data lake. • In this talk, we will present a solution based on Apache Pulsar Functions that distributes the analytics processing across all tiers of the IoT data ingestion pipeline. 5
  4. Pulsar Functions The Apache Pulsar platform provides a flexible, serverless

    computing framework that allows you execute user-defined functions to process and transform data. • Implemented as simple methods, but allows you to leverage existing libraries and code within Java or Python code. • Functions execute against every single event that is published to a specified topic, and write their results to another topic. Forming a logical directed-acyclic graph. • Enable dynamic filtering, transformation, routing and analytics. • Can run anywhere a JVM can, including edge devices 8
  5. Probabilistic Analysis • Often times, it is sufficient to provide

    an approximate value when it is impossible and/or impractical to provide a precise value. In many cases having an approximate answer within a given time frame is better than waiting for an exact answer. • Probabilistic algorithms can provide approximate values when the event stream is either too large to store in memory, or the data is moving too fast to process. • Instead of requiring to keep such enormous data on-hand, we leverage algorithms that require only a few kilobytes of data. 11
  6. Data Sketches • A central theme throughout most of these

    probabilistic data structures is the concept of data sketches, which are designed to require only enough of the data necessary to make an accurate estimation of the correct answer. • Typically, sketches are implemented a bit arrays or maps thereby requiring memory on the order of Kilobytes, making them ideal for resource-constrained environments, e.g. on the edge. • Sketching algorithms only need to see each incoming item only once, and are therefore ideal for processing infinite streams of data. 12
  7. Data Sketch Properties • Configurable Accuracy • Sketches sized correctly

    can be 100% accurate • Error rate is inversely proportional to size of a Sketch • Fixed Memory Utilization • Maximum Sketch size is configured in advance • Memory cost of a query is thus known in advance • Allows Non-additive Operations to be Additive • Sketches can be merged into a single Sketch without over counting • Allows tasks to be parallelized and combined later • Allows results to be combined across windows of execution 13
  8. Sketch Example • Let’s walk through an demonstration to show

    exactly what I mean by sketches and show you that we do not need 100% of the data in order to make an accurate prediction of what the picture contains • How much of the data did you require to identify the main item in the picture? 14
  9. Event Frequency • A common statistic computed is the frequency

    at which a specific element occurs within an endless data stream with repeated elements, which enables us to answer questions such as; “How many times has element X occurred in the data stream?”. • Consider trying to analyze and sample the IoT sensor data for just a single industrial plant that can produce millions of readings per second. There isn’t enough time to perform the calculations or store the data. • In such a scenario you can chose to forego an exact answer, which will we never be able to compute in time, for an approximate answer that is within an acceptable range of accuracy. 17
  10. Count-Min Sketch • The Count-Min Sketch algorithm uses two elements:

    • An M-by-K matrix of counters, each initialized to 0, where each row corresponds to a hash function • A collection of K independent hash functions h(x). • When an element is added to the sketch, each of the hash functions are applied to the element. These hash values are treated as indexes into the bit array, and the corresponding array element is set incremented by 1. • Now that we have an approximate count for each element we have seen stored in the M-by-K matrix, we are able to quickly determine how many times an element X has occurred previously in the stream by simply applying each of the hash functions to the element, and retrieving all of the corresponding array elements and using the SMALLEST value in the list are the approximate event count. 18
  11. K-Frequency-Estimation, aka “Heavy Hitters” • A common use of the

    Count-Min algorithm is maintaining lists of frequent items which is commonly referred to as the “Heavy Hitters”. • The K-Frequency-Estimation problem can also be solved by using the Count-Min Sketch algorithm. The logic for updating the counts is exactly the same as in the Event Frequency use case. • However, there is an additional list of length K used to keep the top-K elements seen that is updated. 20
  12. Pulsar Function: Top K • Each of the hash functions

    are applied to the element. These hash values are treated as indexes into the bit array, and the corresponding array element is set incremented by 1. • Calculate the event frequency for the element as we did in the event frequency use case. However, this time we take the SMALLEST value in the list are use that as the approximate event count. • Compare the calculated event frequency of this element against the smallest value in the top-K elements array, and if it is LARGER, remove the smallest value and replace it with the new element. 21
  13. Anomaly Detection • The most anomaly detectors use a manually

    configured threshold value that is not adaptive to even simple patterns or variances. • Instead of using a single static value for our thresholds, we should consider using quantiles instead. • In statistics and probably, quantiles are used to represent probability distributions. The most common of which are known as percentiles. 23
  14. Anomaly Detection with Quantiles • The data structure known as

    t-digest was developed by Ted Dunning, as a way to accurately estimate extreme quantiles for very large data sets with limited memory use. • This capability makes t-digest particularly useful for calculating quantiles that can be used to select a good threshold for anomaly detection. • The advantage of this approach is that the threshold automatically adapts to the dataset as it collects more data. 24
  15. Identifying Real-Time Energy Consumption Patterns • A network of smart

    meters enables utilities companies to gain greater visibility into their customers energy consumption. • Increase/decrease energy generation to meet the demand. • Implement dynamic notifications to encourage consumers to use less energy during peak demand periods. • Provide real-time revenue forecasts to senior business leaders. • Identify fault meters and schedule maintenance calls. 27
  16. Summary & Review • IoT Analytics is an extremely complex

    problem, and modern streaming platforms are not well suited to solving this problem. • Apache Pulsar provides a platform for implementing distributed analytics on the edge to decrease the data capture time. • Apache Pulsar Functions allows you to leverage existing probabilistic analysis techniques to provide approximate values, within an acceptable degree of accuracy. • Both techniques allow you to act upon your data while the business value is still high. 36
  17. More Information on Apache Pulsar My Book, Pulsar in Action:

    • https://www.manning.com/books/pulsar-in-action • ApacheCon Discount Code: ctwapachecon19 Apache Pulsar documentation: • http://pulsar.apache.org Streamlio Tutorials: • https://streaml.io/resources/tutorials Streamlio Blogs: • https://streaml.io/blog Slack: • apache-pulsar.slack.com 37