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.
# INNER JOIN
(sum1, sum2, by.x =
, by.y =
, all =
(sum1, sum2, sum1$month1 == sum2$month2,
#| 3| -25| 3| 911|
#| 2| -33| 2| 853|
There’s no commentary, so it’s all script all the time. H/T R-bloggers
We’ve learned a lot by working with customers using SparkML, both internal and external to Microsoft. Customers have found Spark to be a powerful platform for building scalable ML models. However, they struggle with low-level APIs, for example to index strings, assemble feature vectors and coerce data into a layout expected by machine learning algorithms. Microsoft Machine Learning for Apache Spark (MMLSpark) simplifies many of these common tasks for building models in PySpark, making you more productive and letting you focus on the data science.
The library provides simplified consistent APIs for handling different types of data such as text or categoricals. Consider, for example, a DataFrame that contains strings and numeric values from the Adult Census Income dataset, where “income” is the prediction target.
It’s an open source project as well, so that barrier to entry is lowered significantly.
Cucumber allows us to write a portion of our software in a simple, language-based approach that enables all team members to easily read the unit tests. Our focus is on detailing the results we want the system to return. Non-Technical members of the team can easily create, read, and validate the testing of the system.
Often Apache Spark is one component among many in processing data and this can encourage multiple testing frameworks. Cucumber can help us provides a consistent unit testing strategy when the project may extend past Apache Spark for data processing. Instead of mixing the different unit testing strategies between sub-projects, we create one readable agile acceptance framework. This is creating a form of ‘Automated Acceptance Testing’.
Best of all, we are able to create ‘living documentation’ produced during development. Rather than a separate Documentation process, the Unit Tests form a readable document that can be made readable to external parties. Each time the code is updated, the Documentation is updated. It is a true win-win.
It’s an interesting mix. I’m not the biggest fan of BDD but I’m happy that this information is out there.
Based on our experience, S3’s availability has been fantastic. Only twice in the last six years have we experienced S3 downtime and we have never experienced data loss from S3.
Amazon claims 99.999999999% durability and 99.99% availability. Note that this is higher than the vast majority of organizations’ in-house services. The official SLA from Amazon can be found here: Service Level Agreement – Amazon Simple Storage Service (S3).
For HDFS, in contrast, it is difficult to estimate availability and durability. One could theoretically compute the two SLA attributes based on EC2’s mean time between failures (MTTF), plus upgrade and maintenance downtimes. In reality, those are difficult to quantify. Our understanding working with customers is that the majority of Hadoop clusters have availability lower than 99.9%, i.e. at least 9 hours of downtime per year.
It’s interesting how opinion has shifted; even a year ago, the recommendation would be different.
We collaborated with our friends at RStudio to enable sparklyr to seamlessly work in Databricks clusters. Starting with sparklyr version 0.6, there is a new connection method in sparklyr:
databricks. When calling
spark_connect(method = "databricks")in a Databricks R Notebook, sparklyr will connect to the spark cluster of that notebook. As this cluster is fully managed, you do not need to specify any other information such as version, SPARK_HOME, etc.
I’d lean toward sparklyr over SparkR because of the former’s tidyverse-centric view.