ETL the easy way

Moving Data from Hadoop into Solr or Data Warehouses


Clouderas Wolfgang Hoschek introduces a new open source library, Morphlines, for creating MapReduce pipelines with minimum fuss.

Here’s a guest post by Cloudera’s Wolfgang Hoschek, who’s today introducing a new open source library: Morphlines, for creating MapReduce pipelines with minimum fuss. 

When bringing Apache Hadoop infrastructure into your environment, integration with existing systems is a paramount concern. That integration, of course, most commonly takes the form of building new data movement and transformation pipelines between the Hadoop Distributed File System (HDFS) and other data endpoints, such as relational data warehouses, distributed data stores (such as Apache HBase), and Apache Solr-based enterprise search servers.

Building these pipelines has long been a common task for Java developers (long before Hadoop even existed, in fact) – so much so that writing custom data movement applications in Java is often considered a core competency. Now, with Hadoop penetrating the data center, building new pipelines that bring Hadoop into the data movement network (or integrating a Hadoop cluster with existing ones), this function is reaching a new apex in popularity.

For those developers, however, writing these custom apps is very often an exercise in reinventing the wheel. Because data formats, sources, and transformation requirements are so varied, re-purposing code is rarely an option. Furthermore, when Hadoop is involved as an endpoint, it’s necessary to know your way around the MapReduce API and be familiar with MapReduce programming.

In this article, I will describe a new open source library, Cloudera Morphlines, that reduces the time and skills necessary to integrate, build, and change Hadoop processing applications that extract, transform, and load data into HDFS, Solr, HBase, enterprise data warehouses, or analytic applications. If you want to integrate, build, or facilitate transformation pipelines without a lot of Java programming and without substantial MapReduce skills, and get the job done with a minimum amount of fuss and support costs, Morphlines is for you.

What is the Morphlines Use Case?

Earlier this year, Cloudera engineers developed Morphlines in support of the new Cloudera Search offering (in beta, at the time of writing), which unites Solr with Hadoop to enable natural-language search across data in HDFS or HBase. Since the launch of Cloudera Search, Morphlines development has graduated into the Cloudera Development Kit (CDK) in order to make the technology accessible to a wider range of users, contributors, integrators, and products beyond Search. The CDK is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem (and hence a perfect home for Morphlines).  

Morphlines powers a variety of ETL data flows from Apache Flume and MapReduce into Solr or data warehouses and other end points. Flume covers the real time case, whereas MapReduce covers the batch processing case.

What is a Morphline?

A “morphline” is a rich configuration file that makes it easy to define a transformation chain that consumes any kind of data from any kind of data source, processes the data, and loads the results into a Hadoop component. It replaces Java programming with simple configuration steps, and correspondingly reduces the cost and integration effort associated with developing, maintaining, or integrating custom ETL projects.

Morphlines is a library, embeddable in any Java codebase. An individual morphline is an in-memory container of transformation commands. Commands are plugins to a morphline that perform tasks such as loading, parsing, transforming, or otherwise processing a single record. A record is an in-memory data structure of name-value pairs with optional blob attachments or POJO attachments. The framework is extensible and integrates existing functionality and third-party systems in a simple and straightforward manner.

Currently, Morphlines works with multiple indexing workloads, but could easily be embedded into Apache Crunch, Apache HBase, Cloudera Impala, Apache Pig, Apache Hive, or Apache Sqoop. Your feedback is welcome and in fact crucial, so please let us know where you see more opportunities for integration going forward! 

Next, let’s take a look at the Morphlines processing and data models.

Processing Model

Morphlines can be seen as an evolution of Unix pipelines where the data model is generalized to work with streams of generic records, including arbitrary binary payloads. A morphline is an efficient way to consume records (e.g. Flume events, HDFS files, RDBMS tables, or Apache Avro objects), turn them into a stream of records, and pipe the stream of records through a set of easily configurable transformations on the way to a target application such as Solr.

The Morphlines framework ships with a set of frequently used high-level transformation and I/O commands that can be combined in application-specific ways. The plugin system allows the adding of new transformations and I/O commands and integrates existing functionality and third-party systems in a straightforward manner.


The CDK ships an efficient runtime that compiles a morphline on the fly. The runtime executes all commands of a given morphline in the same thread. Piping a record from one command to another implies just a cheap Java method call. In particular, there are no queues, no handoffs among threads, no context switches, and no serialization between commands, which minimizes performance overhead.

Data Model


Not only structured data, but also binary data, can be passed into and processed by a morphline. By convention, a record can contain an optional field named _attachment_body, which can be a Java or Java byte[]. Optionally, such binary input data can be characterized in more detail by setting the fields named _attachment_mimetype (such as application/pdf) and _attachment_charset (such as “UTF-8”) and _attachment_name (such as “cars.pdf”), which assists in detecting and parsing the data type. (This is similar to the way email works.)



Commands can access all record fields. For example, commands can parse fields, add fields, remove fields, rename fields, find and replace values of existing fields, split a field into multiple fields, split a field into multiple values, or drop records. Often, regular expression-based pattern matching is used as part of the process of acting on fields. The output records of a command are passed to the next command in the chain. A command has a Boolean return code, indicating success or failure.

For example, consider the case of a multi-line input record: a command could take this multi-line input record and divide the single record into multiple output records, one for each line. This output could then later be further divided using regular expression commands, splitting each single line record out into multiple fields in application specific ways.

A command can extract, clean, transform, join, integrate, enrich and decorate records in many other ways. For example, a command might join records with external data sources such as relational databases, key-value stores, local files or IP Geo lookup tables. It might also perform tasks such as DNS resolution, expand shortened URLs, fetch linked metadata from social networks, perform sentiment analysis and annotate the record accordingly, continuously maintain statistics for analytics over sliding windows, or compute exact or approximate distinct values and quantiles.

A command can also consume records and pass them to external systems. For example, a command might load records into Apache Solr or write them to a MapReduce Reducer, or load them into an Enterprise Data Warehouse or a Key Value store such as HBase, or pass them into an online dashboard, or write them to HDFS.

The CDK includes several maven modules that contain morphline commands for flexible log file analysis, single-line records, multi-line records, CSV files, JSON, commonly used HDFS file formats Avro and Hadoop Sequence Files, regular expression based pattern matching and extraction, operations on record fields for assignment and comparison, operations on record fields with list and set semantics, if-then-else conditionals, string and timestamp conversions, scripting support for dynamic java code, a small rules engine, logging, metrics and counters, integration with Solr including SolrCloud, integration and access to the large set of file formats supported by the Apache Tika parser library, auto-detection of MIME types from binary data using Tika, and decompression and unpacking of arbitrarily nested container file formats, among others. These are described in detail in the Cloudera Morphlines Reference Guide.

Embedding into a Host System

A morphline has no notion of persistence, durability, distributed computing, or node failover — it’s basically just a chain of in-memory transformations in the current thread. There is no need for a morphline to manage multiple processes, nodes, or threads because this is already addressed by host systems such as MapReduce, Flume, or Storm. However, a morphline does support passing notifications on the control plane to command subtrees. Such notifications include BEGIN_TRANSACTION, COMMIT_TRANSACTION, ROLLBACK_TRANSACTION, and SHUTDOWN.


The morphline configuration file is implemented using the HOCON format (Human Optimized Config Object Notation) developed by HOCON is basically JSON slightly adjusted for the configuration file use case. HOCON syntax is defined at the HOCON github page.


If you’ve got any questions, please do ask us. The best place is to do that is in the Community Forum for CDK. Alternatively, if you are trying out Cloudera Search, post your questions here.

Wolfgang Hoschek is a Software Engineer on the Platform team and the lead developer on Morphlines.

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