Tag Archives: tez

Comparing Apache Tez and Microsoft Dryad

Leave a reply

This post is carried over from my earlier blog site here. I am migrating posts that seem to have gathered most hits there.

Hortonworks has been blogging about a framework called Tez, a general purpose data processing framework. Reading through the posts, I was reminded of a similar framework that had come from Microsoft Research a while back called Dryad. This blog post is an attempt at comparing them.

In order to structure the comparison, I am trying to express the points under the following topics: historical perspective, features, concepts, and architecture.

Historical Perspective

Both Tez and Dryad define distributed, data parallel computing frameworks that lay an emphasis on modelling data flow. A data processing ‘job’ in either is defined as a graph. The vertices of the graph represent computational processes, with the edges connecting them describing input they receive and output they send out from / to other computational vertices or data sources / sinks. Both systems attempt to provide an efficient execution environment for running these jobs, abstracting users away from needing to handle common distributed computing requirements such as communication, fault tolerance, etc.

At the time of its introduction, Dryad was possibly Microsoft’s view on how to build such a framework from ground up. In contrast to Hadoop, Dryad attempted even then to provide a framework that wasn’t restricted to just one model (MapReduce) of computation. Dryad was inspired by a variety of data processing systems including MPP databases, data parallel programs on GPUs, and MapReduce as well. It attempted to build a system that could express all these kinds of computation.

Tez was introduced as a generalisation of the MapReduce paradigm that had dominated Hadoop computation for several years. However, it seems to be inspired more by data flow frameworks like Dryad. It was enabled immensely by the separation of concerns brought to the Hadoop MapReduce layer in the form of Apache YARN, that separated cluster resource management from distributed job management, enabling more models than just MapReduce. A direct motivation for Tez was the Stinger initiative, launched to build a faster version of Apache Hive. Specifically, the idea was to enable expressing a HQL query as a single Tez job, rather than multiple MapReduce jobs, thereby avoiding the overhead of launching multiple jobs and also incurring the I/O overhead of having to store data between jobs on HDFS.

Features

Tez and Dryad share several features, such as:

  • The DAG model being the specification choice for a job
  • A flexible / pluggable system where the framework tries to give the user control of the computation, nature of input and output, etc.
  • Supporting multiple inputs and outputs for a vertex (that enable SQL like joins to be expressed, and various forms of data partitioning like the shuffle sort phase of Hadoop MapReduce)
  • An ability to modify the DAG at runtime based on feedback from executing part of the graph. The runtime modification is primarily used for improving the efficiency of execution in both systems. For e.g. in Dryad, this was used to introduce intermediate aggregator nodes (akin to the combiner concept in Hadoop MapReduce), while in Tez, this is being used as a way to optimise the number of reducers or when they would get launched.

Dryad was built from ground up without a supporting resource management or scheduling framework, and some of its ‘features’ are present in or shared by other layers of the Hadoop stack like YARN. In addition to those, Dryad allowed one specific optimisation through which processing nodes can execute concurrently, co-located and connected via  shared memory or pipes.

Tez on its hand, expands on learnings from the Hadoop MapReduce framework. For example, it expands on a feature available with MapReduce called JVM reuse, whereby ‘containers’ launched to run the vertex programs of Tez can be reused for multiple Tez tasks. It even allows sharing data between these tasks via an ‘Object Registry‘ without needing to have them run concurrently.

Concepts

Naturally, the core concepts of a Graph are common between the systems.

In Tez:

  • A vertex is defined by the input, output and processor associated with it.
  • The logical and physical manifestations of a graph are explicitly separated. Specifically, the inputs and outputs are of two types – a physical type and a logical type. The logical type describes the connection between a vertex pair as per the DAG definition, while the physical type will represent the connection between a vertex pair at runtime. The Tez framework automatically determines the number of physical instances of a vertex in a logical graph.
  • Edges are augmented with properties that relate to data movement (for e.g. multicast output between connected vertices), scheduling (co-schedule, or in sequence) and data source (persistence guarantees on the vertex’s output). Tez expects that by using a combination of these properties, one can replicate existing patterns of computation like MapReduce.
  • In addition to the graph concepts, there is also the concept of an ‘event’. Events are a means for the vertices and the framework to communicate amongst themselves. Events can be used to handle failures, learn about the runtime characteristics of the data or processing, or indicate the availability of data.

In Dryad:

  • Inputs and outputs are considered vertices just like processing vertices.
  • Dryad represents the logical representation of the DAG as a set of ‘stages’. However, this does not seem to be a first class concept to specify the DAG at definition time. Specifically, Dryad expects the specific number of instances of  a vertex at runtime to be defined at definition time.
  • A lot of operators are defined which help to build a graph. For instance:
    • Cloning: is an operation by which a given Vertex is replicated. Such a cloning operation is used to define a physical manifestation of a graph.
    • Composition: is used to define types of data movement patterns (akin to the edge property in Tez)  like round robin data transfer, scatter-gather etc.
    • Merge: is used for defining operations like fork/join etc.
    • Encapsulation: is a way of collapsing a graph into a single vertex, which makes it execute on a single node – used to express concurrent, co-located execution.
  • It appears the idea behind the operators is again to try and define patterns of computation like MapReduce.
  • A ‘channel’ is an abstraction of how data is transferred along an edge. There is support for different types of channels like File, Shared Memory, Pipes etc. This is similar to the physical Input/Output types in Tez.

Architecture

Tez is a YARN application. A Tez job is coordinated by the Tez Application Master (AM). It is comprised of Tez tasks. Each task encapsulates a processor (vertex) of the DAG and all inputs and outputs connected to it. A Tez task is launched within a YARN container. However, in the interest of providing good performance, a single YARN container could be reused for multiple Tez tasks. This is managed by a ‘TezTask’ host. The host also manages a store of objects that can be shared between Tez tasks that run within the host.

The Tez Application Master has a Vertex Manager plugin (that can be customised by the developer) for every type of Vertex. In addition, the AM also maintains a Vertex State Machine. As the state of the DAG changes, the Vertex Manager is invoked by the Application Master, who can then act on the State machine to customise the graph execution.

Another point to note is that Tez relies on YARN’s resource manager and scheduler for initial assignment of containers, etc. However, it has the ability to make the scheduling a two level activity. For example, Tez does come with scheduling capabilities, which it uses for features like container reuse.

Dryad’s architecture includes components that do resource management as well as the job management. A Dryad job is coordinated by a component called the Job Manager. Tasks of a job are executed on cluster machines by a Daemon process. Communication with the tasks from the job manager happens through the Daemon, which acts like a proxy. In Dryad, the scheduling decisions are local to an instance of the Dryad Job Manager – i.e. it is decentralised.

The logical plan for a Dryad DAG results in each vertex being placed in a ‘Stage’. The stages are managed by a ‘Stage manager’ component that is part of the job manager, similar to the Vertex Manager in Tez. The Stage manager is used to detect state transitions and implement optimisations like Hadoop’s speculative execution.

Conclusion

Dryad was discontinued by Microsoft in late 2011. Microsoft has since been contributing to Hadoop. Given the similarities between the two systems, a question is about how Tez’s prospects are going to be different from Dryad. A few points that seem to favour Tez, IMO:

  • Tez rides on years of learning from Hadoop MapReduce and other systems including Dryad. Microsoft recently posted that it contributes to Tez. The expectation then would be that the insights and learnings from systems (including what did not work) will help build a better system.
  • The separation of concerns brought about by YARN potentially helps Tez to focus on problems specific to the graph processing model, while delegating resource management and scheduling decisions to another layer – at least partially.
  • The API for Graph construction in Tez appears a lot simpler and intuitive to understand than the corresponding one in Dryad. Hence, it seems easier to adopt the model from a programmer perspective.
  • Given Tez was launched with a specific initiative of making Hive faster, there is a goal it is working towards, and there seems to already be evidence that Tez is enabling improvements in Hive as shown here.

Personally, I feel it would be good to have Tez succeed and several people who have invested in Hive will be able to see huge improvements in performance from their existing applications.

Acknowledgements

Most of the information for this post has come from the publicly available knowledge in blog posts and published paper. If there is any omission or mis-representation, please do let me know !

An initial draft of this post was reviewed by a few committers at Hortonworks: Siddharth Seth, Bikas Saha, Hitesh Shah and Vinod Kumar Vavilapalli. I am thankful to them for their feedback. While I have incorporated some of it, I felt some others are best explained from their end, possibly as comments. I will notify them once the blog is published.

Specifically calling out two points:

  • Both Sid and Hitesh have called out that there are going to be additional changes to the architecture and features in Tez that will soon be published. As this blog was being written, a new post came out from Hortonworks mentioning a new concept called Tez Sessions. So, be sure to watch out for Hortonworks blogs on Tez for more information.
  • Bikas provided feedback about Tez’s motivation being closer not just to systems like Dryad, but also other data flow systems like Hyracks and Nephele. It may be a good academic exercise to see these other systems as well from a perspective of learning.
facebooktwittergoogle_plusredditpinterestlinkedinmail