Big Data and Stream Processing 101 – Part 1: Overview of Tools and Frameworks

While the number of tools in the Open Source Big Data and Streaming Ecosystem still grows, frameworks that are around for a long time become highly mature and feature rich, some may say “enterprise ready”. Thus, it’s not surprising to me to see a lot of my customers who are new to the whole ecosystem are struggling understanding the basics of each of these tools. The first question always is, “When do I use which tool?”, but this is often not enough without having seen a certain tool in action.

This and a tweet that I recently stumbled upon, were motivation enough for me to explain the most basic things you can do with these tools. Each future blog post will contain a description of the most basic operation of exactly one tool and a detailed explanation of this and only this basic operation and none of the advance features, that might confuse beginners.

One motivation for me to write this blog post.

In this first blog post of the series, I want to categorise the tools – as good as possible – and describe each of them with as few words as possible, ideally less than a full sentence. Then I’ll introduce each of these tools in arbitrary order in subsequent blog posts. This should help anybody get started and then attend trainings or do self study to get to know all the features that are supposed to make our lives easier processing and managing data, and lower the barrier to get started with each of these.

Disclaimer: this overview is highly opinionated, most probably biased by my own experience and definitely incomplete ( = not exhaustive). I’m definitely up for discussion and open for questions on why I put a certain tool into a certain category and why certain categories are named as they are. So don’t hesitate to reach out to me 🙂

Tools and Frameworks

I’m re-using the slides that I recently created for a “lunch and learn” session. You’ll notice that a lot if not all of the tools appear in multiple categories.

( ) parentheses mean that I had some issues and spent some time considering if I really would put this tool in a certain category, because it strictly doesn’t fit. I probably put it there, because it *can* be used or it is often *used in combination* with tools in this category.

[ ] parentheses mean that the tool is not very popular anymore. It might still be supported, highly used and mature, but is just not popular anymore and likely to fade away and being replace by another tool.

All – Categorized by Function

“All” doesn’t mean every past and future existing tool in the ecosystem. All in this article means just all the tools that I consider and that are available in one of the distributions of HDP, CDH or the new Cloudera Data Platform (CDP)

Note: “Technical Frameworks” are not frameworks you’d work with on a daily base or at all. They’re just there and enable the rest of the cluster to work properly or enable certain features. All of the frameworks/tools/projects in this category are very different from each other.

Processing – Categorised by Speed of Data

Here “Data at Rest” means, that data could possibly be old, historic data, while “Streaming Data” considers event based/stream processing – processing of data while it’s on it’s why from creation at the source to the final destination. The final destination could be a “Data at Rest” persistence engine/database.

Databases – Categorised by Latency

Latency here could refer to two different things:

  • How up-to-date the data in the database is
  • How long a query to the database takes to respond with the results

I don’t distinguish those two in this categorisation, which would make this exercise a bit too detailed and tedious. Generally, it’s important to consider both to choose an adequate database for a certain use case.

All – Categorised by Use Case

I chose four typical use cases for this categorisation. A lot of other use cases can be realized

List of All Tools and Frameworks

Again, “All” doesn’t mean all tools currently available in the open source big data ecosystem. “All” means the bulletproof, tested, compatible set of components that easily cover the most common Big Data and Streaming use cases.

Apache NiFi: Manage data flows; get data from A to B and process it on the way with a UI

Apache Spark: Use dataframes to extract, transform and load data, train and evaluate ML models.

Apache Kafka: Publish, persist and subscribe to events

Cloudera Data Science Workbench: Explore data; use Python and Spark; deploy and manage ML models

Apache Hive: Data Warehousing engine; SQL on distributed/object storage

Apache Impala: Data Hub engine; SQL on distributed/object storage

Apache Kudu: Quickly analyse fast data

Apache Oozie: Schedule jobs

Apache Sqoop: Transfer data from and to relational databases

Apache Druid: Analyse realtime data with high performance

Apache Solr: Index and search text

Apache Flink: Process streams of data using a programming interface

Apache Storm: Process streams of data using a programming interface

Apache HBase: Consistent, scalable noSQL database; low latency look-ups; unstructured data

Apache Phoenix: Do SQL on top of HBase

Apache Zeppelin: Notebook application tightly integrated with Hive and Spark

Apache Ranger: Define policies to permit or restrict access to data

Apache Knox: Manage access to services

Apache Atlas: Manage meta data

Part 2: How to Create a Simple Ranger Policy

How to Onboard a New Data Source in Apache Metron

Introduction

Apache Metron aims to be a tool for analysts in a cyber security team to help them defining intelligent alerts, detecting threats and work on them in real-time. This is the first blog post in a row to ease operations and share my experiences with Apache Metron. Thus, it serves as an introduction to Metron.

Technical Introduction

Apache Metron is a cyber security platform making heavy use of the Hadoop Ecosystem to create a scalable and available solution. It utilizes Apache Storm and Apache Kafka to parse, enrich, profile, and eventually index data from telemetry sources, such as network traffic, firewall logs, or application logs in real-time. Apache Solr or Elastic Search are used for random access searches, while Apache Hadoop HDFS is used for long term and analytical storage. It comes with its own scripting language “Stellar” to query, transform and enrich data. A security operator/analyst uses the Metron Management UI to configure and manage input sources as well the Metron Alerts UI to search, filter and group events.

Screen Shot 2018-07-18 at 08.07.45
Metron Alerts UI, showing a few dummy events from a Squid log.

Scope of this Post

Since virtually every data source can be used to generate events, it is natural that the platform operator/analyst wants to add data from new sources over time. I use this post as a small check list, to document considerations for the “onboarding” process of new data sources. You might want to automate this process in a way that works for you. In future posts I will cover the steps in detail.

Onboard a New Data Source

I need to ingest data to Kafka

  • It’s very handy to use Apache NiFi for the ingest part. Just create a data flow consisting of two processors: a simple tcp listener to receive data and a Kafka producer to push the event further into Kafka.
  • I can also push data directly into Kafka if the architecture,  firewall and the source system allow it.
  • If there are no active components on the source system pushing data, I might want to install an instance of MiNiFi on my source system.
Screen Shot 2018-07-19 at 10.32.29
Simple example of a data ingest into Kafka via NiFi

Before I can ingest data into Kafka, I need a new Kafka topic

  • While the Kafka topics “enrichments” and “indexing” Kafka topics will be used by all data sources, the parser topics are specific to a data source.
  • I create a topic named “squid” with a number of partitions that corresponds to the amounts of data I receive.

To make the events searchable, i.e., to store the events into Apache Solr, I need to create a new Solr collection (or Elastic Search template)

  • For each parser Storm topology and parser Kafka topic, there is a parser Solr collection.
  • I add a few specific fields common to all Metron Solr collections and optionally define data source specific fields in the schema.xml.
  • I create a new collection named “squid” with a number of shards that corresponds to the amount of data I receive.

I define my parser in the Metron Management UI

  • I click the “+” button in the right bottom corner of the Metron Management UI.
  • I configure my parser by choosing a Java class and/or define a Grok pattern, insert a sample and check if the parsed output is what I expect.
  • I configure the parser: Kafka topic name, Solr collection name, parser config, enrichment defintions, threat intel logic, transformations, parallelism.
  • I save the parser configuration and press the “Play” button next to the new parser to start it.
Screen Shot 2018-07-18 at 08.37.04 1
Metron Management UI with my configured parsers. Currently only the Squid parser is running that produces the events in the first screenshot.

Outlook

I hope this post was helpful and informative. For questions I refer to the documentation, future posts, the Metron mailing list or post a question below.

How to Troubleshoot an Apache Storm Topology

Apache Storm is a real-time, fault-tolerant, event-based streaming framework and platform that runs your code in a highly parallelized way on distributed nodes. It’s all about Spouts (processing units to read from data sources) and Bolts (general processing units). Storm is often used to read data from Apache Kafka and write the results back to Kafka or to a data store. Apache Storm and Apache Kafka are the work horses of the cyber security platform Apache Metron. Storm is also being used internally by the Streaming Analytics Manager (SAM)

This article guides you through the debugging process and points you to the places you need to tweak your configuration to get your topology up and running in a kerberized environment in case certain errors occur. For basic information on how to authenticate your application check out the reference implementation by Pierre Villard on his Github page.

Prerequisites

I assume that you start from a certain point:

  • Your Storm cluster and the services you communicate with (Kafka, Zookeeper, HBase) is up and running as well as secure, i.e., the authentication happens through the Kerberos protocol.
  • Your Storm cluster is configured to run topologies as the OS user corresponding to the Kerberos principal who submitted the topology. (See: “Run worker processes as user who submitted the topology” in the excellent article of the Storm documentation)
  • Your topology (written in Java) is ready to be deployed and authentication is put in place.

Debugging Process

  • Use the Storm UI to check if the topology’s workers are throwing any errors and on which machine they are running! The worker’s log files are stored on the machine the worker is running in /var/log/storm/workers-artifacts/<topology-name><unique-id>/<port-number>/worker.log.
  • Check the input data and output data of your Storm topology. In case you are using Kafka, connect via the Kafka console consumer and read from the input and the output topic of your topology! If you don’t see any events in the input Kafka topic, you should check upstream for errors. If you do see input events, but no output events, refer to your topology logs described in the item above. If you do see output events, check if they have the expected format (data format, number and kind of fields are correct, fields contain data that makes sense as opposed to null values)
Screen Shot 2018-07-16 at 19.49.37
# List Kafka topics:
bin/kafka-topics.sh --zookeeper <zookeeper.hostname&gt;:<zookeeper.port&gt; --list

# print messages as they are written on stdout from input topic
bin/kafka-console-consumer.sh --bootstrap-server <kafka.broker.hostname&gt;:<kafka.broker.port&gt; --topic input

# print messages as they are written on stdout from output topic
bin/kafka-console-consumer.sh --bootstrap-server <kafka.broker.hostname&gt;:<kafka.broker.port&gt; --topic output




Possible Error Scenarios

Authentication Errors Exception

Caused by: javax.security.auth.login.LoginException: Could not login: the client is being asked for a password, but the Kafka client code does not currently support obtaining a password from the user. not available to garner authentication information from the user

Your topology is being submitted and the supervisor tries to start and initialize the Spouts and Bolts in the worker process based on the configuration you provided. When this error occurs the worker process is killed and the supervisor tries to spawn a new worker process. On the machine the worker is supposed to run, you can see a worker process popping up with a certain PID (ps aux | grep <topology_name>). A few seconds later this process is killed and a process exactly as the old one is started with a different PID. You can also tail the worker log and see this error message. Soon afterwards the “Worker has died” message appears. This can happen for various reasons:

  • The OS user running the topology does not have the permission to read the keytabs configured in the jaas config file. Check with ps aux or top which user is running and check if the keytab has the correct POSIX attributes. Usually it should be read-only by the owning user (-r– — — <topology-user><topology-user>)
  • The jaas configuration points to the wrong keytabs to be used for authentication and the OS user does not have permission to those. Check with ps aux which jaas file is configured. You might find an option there. Check if this jaas config file has the desired authentication options configured. If not configure your own and pass it to the topology.
-Djava.security.auth.login.config=/etc/storm/conf/client_jaas.conf