This is a hands-on tutorial that can be followed along by anyone with programming experience. If your programming skills are rusty, or you are technically minded but new to programming, we have done our best to make this tutorial approachable. Still, there are a few prerequisites in terms of knowledge and tools.
The following tools will be used:
Git—to manage and clone source code
Docker—to run some services in containers
Java 8 (Oracle JDK)—programming language and a runtime (execution) environment used by Maven and Scala
Maven 3—to compile the code we write
Some kind of code editor or IDE—we used the community edition of IntelliJ while creating this tutorial
Scala—programming language that uses the Java runtime. All examples are written using Scala 2.12. Note: You do not need to download Scala.
The Hello World of streaming apps is a Twitter client.
VariantSpark RF starts by randomly assigning subsets of the data to Spark Executors for decision tree building (Fig 1). It then calculates the best split over all nodes and trees simultaneously. This implementation avoids communication bottlenecks between Spark Driver and Executors as information exchange is minimal, allowing it to build large numbers of trees efficiently. This surveys the solution space appropriately to cater for millions of features and thousands of samples.
Furthermore, VariantSpark RF has memory efficient representation of genomics data, optimized communication patterns and computation batching. It also provides efficient implementation of Out-Of-Bag (OOB) error, which substantially simplifies parameter tuning over the computationally more costly alternative of cross-validation.
We implemented VariantSpark RF in scala as it is the most performant interface languages to Apache Spark. Also, new updates to Spark and the interacting APIs will be deployed in scala first, which has been important when working on top of a fast evolving framework.
Give it a read. Thankfully, I exhibit few of the traits of the degenerative disease known as Hipsterism.
Distribute R computations using
spark_apply()to execute arbitrary R code across your Spark cluster. You can now use all of your favorite R packages and functions in a distributed context.
Connect to External Data Sources using
I’ve been impressed with sparklyr so far.
DataFrame is an abstraction which gives a schema view of data. Which means it gives us a view of data as columns with column name and types info, We can think data in data frame like a table in the database.
Like RDD, execution in Dataframe too is lazy triggered.
Read on to learn more about Resilient Distributed Datasets, DataFrames, and DataSets.
Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These networks not only take a lot of compute and storage resources (even with a cluster of GPUs, they can take weeks to train), but also require a lot of data. With only 30000 images, it is not practical to train such a complex model on Caltech-256 as there are not enough examples to adequately learn so many parameters. Instead, it is better to employ a method called transfer learning, which involves taking a pre-trained model and repurposing it for other use cases. Transfer learning can also greatly reduce the computational burden and remove the need for large swaths of specialized compute resources like GPUs.
It is possible to repurpose these models because convolutional neural networks tend to learn very general features when trained on image datasets, and this type of feature learning is often useful on other image datasets. For example, a network trained on ImageNet is likely to have learned how to recognize shapes, facial features, patterns, text, and so on, which will no doubt be useful for the Caltech-256 dataset.
This is a longer post, but on an extremely interesting topic.
Enabling Spark Streaming’s checkpoint is the simplest method for storing offsets, as it is readily available within Spark’s framework. Streaming checkpoints are purposely designed to save the state of the application, in our case to HDFS, so that it can be recovered upon failure.
Checkpointing the Kafka Stream will cause the offset ranges to be stored in the checkpoint. If there is a failure, the Spark Streaming application can begin reading the messages from the checkpoint offset ranges. However, Spark Streaming checkpoints are not recoverable across applications or Spark upgrades and hence not very reliable, especially if you are using this mechanism for a critical production application. We do not recommend managing offsets via Spark checkpoints.
The authors give several options, so check it out and pick the one that works best for you.
In order to work with Spark H2O using rsparkling and sparklyr in R, you must first ensure that you have both sparklyr and rsparkling installed.
Once you’ve done that, you can check out the working script, the code for testing the Spark context, and the code for launching H2O Flow. All of this information can be found below.
It’s a short post, but it does show how to kick off a job.
Since Apache Spark separates compute from storage, every Spark Job requires a set of credentials to connect to disparate data sources. Storing those credentials in the clear can be a security risk if not stringently administered. To mitigate that risk, Databricks makes it easy and secure to connect to S3 with either Access Keys via DBFS or by using IAM Roles. For all other data sources (Kafka, Cassandra, RDBMS, etc.), the sensitive credentials must be managed by some other means.
This blog post will describe how to leverage an IAM Role to map to any set of credentials. It will leverage the AWS’s Key Management Service (KMS) to encrypt and decrypt the credentials so that your credentials are never in the clear at rest or in flight. When a Databricks Cluster is created using the IAM Role, it will have privileges to both read the encrypted credentials from an S3 bucket and decrypt the ciphertext with a KMS key.
That’s only one data source, but an important one.
The low latency and an easy-to-use event time support also apply to Kafka Streams. It is a rather focused library, and it’s very well-suited for certain types of tasks. That’s also why some of its design can be so optimized for how Kafka works. You don’t need to set up any kind of special Kafka Streams cluster, and there is no cluster manager. And if you need to do a simple Kafka topic-to-topic transformation, count elements by key, enrich a stream with data from another topic, or run an aggregation or only real-time processing — Kafka Streams is for you.
If event time is not relevant and latencies in the seconds range are acceptable, Spark is the first choice. It is stable and almost any type of system can be easily integrated. In addition it comes with every Hadoop distribution. Furthermore, the code used for batch applications can also be used for the streaming applications as the API is the same.
Read on for more analysis.
Flink and Spark are in-memory databases that do not persist their data to storage. They can write their data to permanent storage, but the whole point of streaming is to keep it in memory, to analyze current data. All of this lets programmers write big data programs with streaming data. They can take data in whatever format it is in, join different sets, reduce it to key-value pairs (map), and then run calculations on adjacent pairs to produce some final calculated value. They also can plug these data items into machine learning algorithms to make some projection (predictive models) or discover patterns (classification models).
Streaming has become the product-level battleground in the Hadoop ecosystem, and it’s interesting to see the different approaches that different groups have taken.