Multiplexing in Thrift: Enhancing thrift to meet Enterprise expectations- Impetus White Paper

Multiplexing in Thrift: Enhancing
Thrift to Meet Enterprise
Expectations
Abstract
Thrift [1] is an open source library that expedites development
and implementation of efficient and scalable back-end services.
Its lightweight framework and support for cross language
communication makes it more robust and efficient than other
RPC frameworks like SOA (REST/SOAP) for many operations.
However, Thrift’s capabilities are challenged by emerging
enterprise solutions like Big Data that impose high
maintainability and administrative overheads on an enterprise
hosting multiple services over the network, due to its limitation
of hosting one service per port.
This paper addresses the challenge and details the approach that
Impetus has devised, to enhance the caliber of Thrift and enable
it to meet enterprise expectations.
Impetus Technologies, Inc.
www.impetus.com
W H I T E P A P E R

Multiplexing in Thrift: Enhancing thrift to meet enterprise expectations
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Table of Contents
Introduction..............................................................................................3
What’s so special about Thrift? ................................................................3
Thrift is powerful, yet lacks the prowess..................................................4
Adding charm to the glorious API through multiplexing ..........................5
The approach...............................................................................6
Components.................................................................................6
How to use thrift multiplexing..................................................................9
Creating a multiplexing server with a lookup registry.................9
Making a wise investment lucrative .......................................................13
Summary.................................................................................................14

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Introduction
Thrift is a very lightweight framework for developing and accessing remote
services that are highly reliable, scalable and efficient in communicating across
languages.
Thrift API is extensively used for creating services like search, logging, mobile,
ads, and the developer platform across various enterprises. The services of
various Big Data open source initiatives like HBase [6], Hive [7] and Cassandra
[8] are hosted on Thrift. Its simplicity, versioning support, development
efficiency, and scalability make it a strong contender in the SOA market, helping
it to compete successfully against more established integration approaches and
products.
Thrift has the capability of supporting a large number of functions,
communicating across languages for each service. This capability can be further
enhanced by extending Thrift support to host multiple services on each server.
In this white paper, we look at how the capabilities of Thrift can be enhanced
to make optimum use of enterprise resources. We have also presented a
framework that can enable the creation of server hosting multiple services,
registration of service(s) and lookup of service(s), based on standard context.
What’s So Special About Thrift?
There are various flavors of RPC implementations available in the open source
arena, including Thrift, Avro [2], MessagePack [3], Protocol Buffers [4], BSON
[5], etc. Each of RPC implementation libraries has its own pros and cons. Ideally
we should select the RPC library according to specific enterprise solution
requirements of the project.
Some of the features that any RPC implementation aspires for are:
1. Cross Platform communication
2. Multiple Programming Languages
3. Support for Fast protocols (local, binary, zipped, etc.)
4. Support for Multiple transports
5. Flexible Server (configuration for non-blocking, multithreading, etc.)
6. Standard server and client implementations
7. Compatibility with other RPC libraries

4
8. Support for different data types and containers
9. Support for Asynchronous communication
10. Support for dynamic typing (no schema compilation)
11. Fast serialization
Compared to other RPC implementations, Thrift, Avro and MessagePack are the
top contenders, serving most of the above listed requirements.
In an Avro implementation, out-of-band schema can become overkill for
infrequent conversations between a server and client. MessagePack,
meanwhile, is weaker than Thrift on account of a paucity of data type
containers, being inherently JSON-based and no type checking with schema.
On the other hand, support for various protocols and transports, configurable
servers, simple standardized IDL, and battle -tested integration with Big Data
NoSQL data stores like Cassandra make Thrift a powerful contender and
preferred RPC implementation in enterprise solutions.
Thrift is Powerful, Yet Lacks the Prowess
Despite being a powerful and efficient cross language communication tool,
Thrift’s services are challenged by high administrative and maintenance
overheads. The fact remains that every Thrift server is capable of exposing only
a single service at a time. In order to host multiple functions, Thrift provides
organizations with the following two options
1) Write a monolithic, unwieldy implementation and host it as single
service
2) Host multiple small services across a series of ports
fig1.1 : Option 1- Write a monolithic, unwieldy implementation and host it as single service

5
If an enterprise opts to follow the first option (ref fig 1.1) then, monolithic and
unwieldy implementation elevates the development cost of the solution. Since
the complexity of the solution keeps on growing with the addition of every new
service. Return on Investment (ROI) is adversely affected by high maintenance
overheads.
fig1.2 : Option 2 - Host multiple small services across a series of ports
If an enterprise opts for the second option, the number of ports consumed for
hosting multiple services will be high. Since ports are a limited enterprise
resource, that needs to be used judiciously, this poses a serious concern. This
option will therefore be challenged by high administrative and maintenance
overheads. Also, to prevent overheads related to connection setups on each
call, clients have to maintain too many connections (at least one to each port).
With the addition of every new service, a new port has to be opened on the
firewall. The advantage of Thrift’s flexible design for the solution is thus
challenged by high administrative overheads.
Adding Charm to the Glorious API through
Multiplexing
The need of the hour is to realize and harness the potential of the Thrift API, by
overcoming its limitation of hosting a single service on each server. The solution
presented through this White Paper is an attempt to create a framework that
can enable Java developers to create and host multiple services on each server.
This solution also presents a lookup framework that any Java client/server can
use for quick and easy lookup of services that is hosted on each server and a
way to access the same.

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The approach
The baseline approach is to assign a symbolic name to each service which is
referred to as 'service context' in this Paper. This will help us in hosting multiple
services on each server where each service can be recognized by its respective
service context. A client using lookup service should be able to fetch the
appropriate service context and use the same for directing the service call to the
respective servant.
Components
The solution has extended the Thrift API[version 0.9.0] to introduce some of the
new components (highlighted with red boundaries in fig1.3) mentioned below:
Multiplexer
Multiplexer is the processor that is at the heart of this solution. This
component acts as a server side request broker and is responsible for
identifying the service that the client has requested for, based on the
service context propagated by the client. This component maintains a
mapping between the service context and the service. While processing
any request, it reads the service context from the underlying protocol
and based on the mapping, directs the request to the appropriate
service.
fig1.3 : Thrift Multiplexing
Protocol
In our approach, we have made our solution transport and protocol
agnostic. We have created a wrapper around the underlying protocol
(any Protocol instance) that is capable of embedding service context to

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the message on the client side and fetching the same on the server side.
Thus, we have added a new class TMultiplexProtocol as a wrapper
around the existing TProtocol that overrides the behavior of
writeMessageBegin (TMessage) and readMessageBegin() methods. Any
client that has to communicate with TMultiplexer needs to wrap the
underlying protocol using the TMultiplexProtocol instance.
Registry and Lookup
In order to reduce the overheads associated with managing the service
context manually, we have created a registry component along with this
solution that is responsible for managing information pertaining to all
services hosted on a particular server. This component is hosted as one
of the service on the underlying multiplexer and can be queried by the
client on the TMultiplexerConstants.LOOKUP_CONTEXT for procuring
relevant information about the hosted services.
The TRegistry interface is the basic client API for querying the lookup
registry. It provides several lookup methods for querying registry based
on service context, service name and regular expression. It also
facilitates users in checking the existence of any service context and
listing all available service contexts with the registry.
TRegistryHelper is an interface for the server API, which is used by the
server for binding, rebinding and unbinding of service context with the
lookup registry. We have provided one basic implementation of the
registry API, TRegistryBase that performs in memory management of
the service context. This component can be extended to override the
default behavior, based on the specific need, and can be used along
with the Factory class. TRegistryClientFactory is the Factory class for
creating the registry client that facilitates remote lookup of registry.
Service Information
The solution uses the URIContext class to capture/represent
information regarding service(s) hosted on a particular server. This
object is capable of transmitting across the network; and hence can be
accessed remotely by the client. Service context, service name and
description are part of the information captured by this object in the
present solution.
Multiplexer-extension for lookup

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fig1.3 : Thrift Multiplexing with Lookup Registry
On its own, Multiplexer is capable of hosting multiple services.
However, managing service information is an overhead for the client as
well as server administrator. To reduce this overhead, we have
introduced a registry component that is capable of managing service
information. In order to leverage the capability of the multiplexer and
registry component in a single processor, we have introduced our new
processor TLookupMultiplexer that is capable of hosting multiple
services along with an additional lookup service based on the registry.
The processor therefore creates an instance of registry with all service
information and exposes it as an additional service to clients. This
enables clients to query registry using Registry API, and accessing the
underlying service using the service context obtained after querying.
Server
We have presented a new abstract server, the TMultiplexingServer,
which is capable of hosting any server implementation on any transport
and any protocol, using TLookupMultiplexer. This class abstracts the
underlying complexities of object creation and exposes two abstract
methods, vis. getServer and configureMultiplexer, to be implemented
by any class extending this class. This class enables a user to identify the
server transport and protocol at the time of the server object creation,

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thus providing an additional degree of flexibility when it comes to
hosting the same server with multiple services on different transport
and protocols with no additional coding effort. The TMultiplexingServer
internally wraps the instance of the TServer, allowing the server startup
and shutdown to be managed in accordance with the requirement.
Source Code
We have extended the Thrift Java library[version 0.9.0] and added a
new source folder by the name ‘ext’ that contains the underlying
implementation of multiplexing components. Also, build.xml has been
amended to compile existing and extended source code. Compatibility
of the solution has additionally been tested with the present stable
version 0.8.0 of Thrift for seamless integration. In order to use the
multiplexing capability of Thrift, one has to download/pull source code
of the extended Thrift library [9] from git-hub and run the ‘ant’
command on the downloaded Thrift Java library. This will generate the
libthrift-xxx.jar in build folder, which can further be used by developers
for creating their enterprise solutions.
How to use Thrift Multiplexing
Creating a multiplexing server with a lookup registry
The multiplexing server can be created by extending
org.apache.thrift.server.TMultiplexingServer class and by implementing the
abstract method configureMultiplexer () and getServer
(TServerTransportserverTransport, TProtocolFactoryprotFactory, TProcessor
processor). The sample code with the illustration is provided below:
Step 1: Creating the server class by extending the TMultiplexingServer class.
public class Server1<T extends TServerTransport, F extends TProtocolFactory>
extends TMultiplexingServer<T, F>
Step 2: Optionally override the default constructor to accept server transport
and protocol
public Server1(T serverTransport, F protFactory) {
super(serverTransport, protFactory);
}
Step 3: Implement the configureMultiplexer() method to configure the lookup
multiplexer. As a part of this configuration, one has to create a list of

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MultiplexerArgs that capture the details of the services that will be hosted on
the server and their respective service information. In the example illustrated
below, we have hosted the HR and Finance services on Server1.
@Override
protected List<MultiplexerArgs<URIContext,
TProcessor>>configureMultiplexer() {
//list of multiplexer arguments
List<MultiplexerArgs<URIContext, TProcessor>> args = new
ArrayList<MultiplexerArgs<URIContext, TProcessor>>();
// configuring HR service context
TProcessor processor = new HRService.Processor<HRServiceImpl>(new
HRServiceImpl());
URIContext context = new URIContext(Constants.HR_CONTEXT,
"HumanResource_Service");
MultiplexerArgs<URIContext, TProcessor> arg = new
MultiplexerArgs<URIContext,TProcessor>(processor, context);
args.add(arg);
// configuring FIN service context
processor = new FinanceService.Processor<FinanceServiceImpl>(new
FinanceServiceImpl());
context = new URIContext(Constants.FIN_CONTEXT, "Finance_Service");
arg = new MultiplexerArgs<URIContext,TProcessor>(processor, context);
args.add(arg);
return args;
}
Step 4: Implement the getServer(…) method to create an instance of the desired
server. In the example below, we are creating an instance of ThreadPoolServer
using the arguments.
@Override
Protected TServer getServer (TServerTransport serverTransport,
TProtocolFactory protFactory, TProcessor processor) {
//creating server args
Args serverArgs= new Args(serverTransport);
serverArgs.protocolFactory(protFactory);
serverArgs.transportFactory(new TTransportFactory());
serverArgs.processor(processor);
serverArgs.minWorkerThreads=1;

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serverArgs.maxWorkerThreads=5;
//creating server instance
Return new TThreadPoolServer(serverArgs);
}
Step 5: Create the instance of a server class, using the appropriate transport and
protocol, and start the server.
public static void main(String[] args) {
//identifying server transport
TServerSocket SERVER1_TRANSPORT = new
TServerSocket(Constants.SERVICE1_PORT);
//identifying server protocol
Factory SERVER1_FACTORY = new TBinaryProtocol.Factory();
//creating server instances for specific transport and protocol
Server1<TServerSocket, TBinaryProtocol.Factory> server1 =
new Server1<TServerSocket, TBinaryProtocol.Factory>(SERVER1_TRANSPORT,
SERVER1_FACTORY);
//starting server
server1.start();
}
Creating a client for querying the registry and using the service context
A Client-to-query multiplexing server registry can be procured from
org.apache.thrift.registry.TRegistryClientFactory class.TRegistryClientFactory is
the convenience class that provides multiplexing client instances. On the client
side, one can use the static method getClient(..) of this factory to procure the
registry client. This can further be used to query registry and identify the
appropriate server for processing the request. The example code provided
below is about a client that retrieves the tax detail of an employee using the
finance service:
public double getTaxDetails(intempId){
TTransport transport = null;
TProtocol protocol = null;
try {
//transport
transport = new TSocket(Constants.SERVICE_IP,
Constants.SERVICE1_PORT, 60);

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//Multiplexing protocol
protocol = Factory.getProtocol(new TBinaryProtocol(transport),
TConstants.LOOKUP_CONTEXT);
//Procuring Registry client
TRegistry client = TRegistryFactory.getClient(protocol);
//opening transport
transport.open();
//querying registry to get context
Set<URIContext> contexts = client.lookupByName("Finance_Service");
//executing the request on appropriate service using the context
if(contexts.size()==1){
URIContext uricontext = contexts.iterator().next();
protocol =
newTMultiplexProtocol(newTBinaryProtocol(transport),uricontext.getContext())
;
com.service.FinanceService.Client finService = new
com.service.FinanceService.Client(protocol);
return finService.getTaxDeductedTillDate(empId);
}
}finally {
if(transport!=null)
//closing transport
transport.close();
}
}

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Making a Wise Investment Lucrative
Thrift is a big plus in today’s enterprise environment, as it addresses all the
challenges imposed by any Big Data solution in an effective manner, and
presents a solution that can be exposed as a service across the network. Most
enterprises have limited ports, especially in the production environment, and
opening new ports involves an associated cost. Using Thrift as an RPC
mechanism for a solution is restrictive, on account of the limited availability of
the ports. Also, various Big Data solutions like Hadoop, Hive, HBase, Cassandra,
NoSQL data stores etc., and other enterprise software such as web servers,
application servers, and ESBs already use up a number of ports. If an enterprise
has to expose its solutions as services (that are using the underlying Big Data) on
the network, then opening extra ports for each service would be ineffective in
terms of cost and resources. This enterprise problem can be effectively
addressed by hosting all the services with the help of Thrift multiplexing that
can reduce the number of ports to one, with very minimal development and
administrative overheads.
An organization investing in this technology is certainly going to reap the benefit
of quick turnaround times and low development costs. Furthermore, the
extensions done for multiplexing make these investments lucrative by reducing
the maintenance and administrative overheads for enterprises. With
multiplexing, multiple services can be hosted on a single Thrift server, thus
cutting maintenance costs over the long run. Modular designing of services can
be undertaken using the capability of multiplexing that can reduce the future
development cost of introducing new service(s)/function(s) or amending
existing services. Hence, multiplexing through its simple approach, not only
makes an investment worthwhile, but also brings added value to business.

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Summary
In recent times Thrift has emerged as a powerful technology for communicating
across programming languages in a reliable and efficient manner. Enterprises
dealing with Big Data and other advanced technologies can use the Thrift
solution to host multiple services on the network by efficiently utilizing
enterprise resources, at low maintenance costs.
References
[1] http://thrift.apache.org/
[2] http://avro.apache.org/
[3] http://msgpack.org/
[4] http://code.google.com/p/protobuf/
[5] http://bsonspec.org/
[6] http://hbase.apache.org/
[7] http://hive.apache.org/
[8] http://cassandra.apache.org/
[9] git://github.com/impetus-opensource/thrift.git
About Impetus
Impetus Technologies is a leading provider of Big Data solutions for the
Fortune 500®. We help customers effectively manage the “3-Vs” of Big Data
and create new business insights across their enterprises.
Website: www.bigdata.impetus.com | Email: bigdata@impetus.com
© 2013 Impetus Technologies, Inc.
All rights reserved. Product and
company names mentioned herein
may be trademarks of their
respective companies.
May 2013

Multiplexing in Thrift: Enhancing thrift to meet Enterprise expectations- Impetus White Paper

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