ScappShots

Last month I gave a five minute talk about GigaSpaces and Terracotta at London’s CloudCamp, an event which was well organized, well attended and well received. If you care to see my talk, follow this link:

http://skillsmatter.com/podcast/cloud-grid/refreshment-break-select-breakout-session

(videos generously hosted by the folks at Skills Matter).  Click around and you’ll find a lot more good content.

A couple of weeks ago I posted a quick example and explanation of a GigaSpaces local transaction.  You can find the post here and get the code here.

In today’s short post I will extend that example to use a distributed transaction.  We’ll do this in two steps: first we’ll break the example; then we’ll fix it.

As a reminder, a GigaSpaces distributed transaction is any transaction that operates on more than one primary space.  In the example code, our client program executed a local transaction when it wrote two instances of TestClass to a single instance remote space.

In that example routing was not a concern because we created the space as unpartitioned, and we did not declare a space routing field. Behind the scenes, however, GigaSpaces selected one (the id field) for us.  You can check this on the Space Browser tab by expanding the GSSimpleTranExample space node, clicking on “Classes”, then clicking on “TestClass”.  The name of Routing Filed will appear on the Classes Info tab just above the table showing the fields (only one in our case) in the class.

Now drop the space using Undeploy Application on the Cluster Runtime tab.  Then recreate it as a partitioned space with two partitions and no backups.  Rerun the client application, and it will fail with this error message:

Exception in thread “main” org.openspaces.core.TransactionDataAccessException: Invalid operation – local transaction spans over multiple spaces – [GSSimpleTranExample_container2:GSSimpleTranExample, GSSimpleTranExample_container1:GSSimpleTranExample] !
You might be using hash based load balancing (partitioned schema) while writing data into multiple spaces and not into a single node.
Please Use Jini Transaction manager with your operations.
; nested exception is net.jini.core.transaction.TransactionException: Invalid operation – local transaction spans over multiple spaces – [GSSimpleTranExample_container2:GSSimpleTranExample, GSSimpleTranExample_container1:GSSimpleTranExample] !
You might be using hash based load balancing (partitioned schema) while writing data into multiple spaces and not into a single node.
Please Use Jini Transaction manager with your operations.

The reason is that GigaSpaces attempted to route each of the two writes to  different partitions, which turned our local transaction into a distributed transaction.  Because we configured the application with a local transaction manager, the transaction fails.

To fix the application we need to specify a distributed transaction manager instead of a local one.  Here’s how:

Find the line in the Spring application context file, GSSimpleTranExample.xml, in which we specify a transaction manager:

<!– @page { margin: 2cm } P { margin-bottom: 0.21cm } –><os-core:local-tx-manager id=“transactionManager” space=“gSSimpleTranExample”/>

and replace it with a line that looks like this:

<!– @page { margin: 2cm } P { margin-bottom: 0.21cm } –>

<os-core:distributed-tx-manager id=“transactionManager” />

Note that the distributed transaction manager, unlike a local transaction manager, is not associated with a particular space.

Now run the client application.  This time it should work.  You can confirm the transactional behaviour using the techniques described in the earlier post.

I’ve mentioned my DICE study – a comparison of eight different solutions to a simple distributed computing problem using GigaSpaces and Terracotta – in several posts.  It is finally available for download.  If you want to take a look, please follow the link to the scapps website.  Access to the paper is quick and easy.

Introduction

One way to think about GigaSpaces¹ is as a sort of database management system for maintaining and accessing data spread across a set of caches. Of course this view ignores many important capabilities of the GigaSpaces framework, but it is a useful perspective for considering GigaSpaces’ transactional features.

GigaSpaces offers transactional control over access to data in its spaces. When the right combinations of API features are employed, data operations assume ACID characteristics.

As a reminder, ACID is an acronym standing for “atomic, consistent, isolated and durable”. The term refers to the behaviour that is generally expected from transactional systems. Quoting Wikipedia:

• Atomicity: Either all the tasks in a transaction must be done, or none of them. The transaction must be completed, or else it must be undone (rolled back).

• Consistency: Every transaction must preserve the integrity constraints — the declared consistency rules — of the database. It cannot place the data in a contradictory state.

• Isolation: Two simultaneous transactions cannot interfere with one another. Intermediate results within a transaction are not visible to other transactions.

• Durability: Completed transactions cannot be aborted later or their results discarded. They must persist through (for instance) restarts of the DBMS after crashes

It is important to understand that GigaSpaces transactions govern only the state of data in the spaces managed by GigaSpaces. Unlike the transactional support provided by heap-oriented products such as Terracotta and Kabira, GigaSpaces transactions do not provide guarantees concerning access to or the state of heap memory in a GigaSpaces application.

Lineage

GigaSpaces’ transaction support derives from three sources:

1. Jini – At its core, GigaSpaces is an implementation of the JavaSpaces specification, which is a component of the Jini specification. Jini was designed to allow heterogeneous software and hardware devices to interact. The Jini specification (and reference implementation) include a facility for Jini-compliant devices to participate in distributed transactions. GigaSpaces has inherited and extended this capability.

2. JTA (Java Transaction API) – Jini provides the ability to orchestrate transactions among Jini-compliant participants such as JavaSpaces. Sometimes, however, it may be necessary to engage in a transaction both participants that are Jini-compliant and participants that do are not Jini-compliant. For example, a GigaSpaces application might need to remove a data entry from a space and insert a corresponding row into a table in an RDBMS. Most RDBMSs support a distributed transaction protocol called XA that allows them to participate in transactions with otherwise independent participants. Using JTA, GigaSpaces can participate in XA distributed transactions.

3. Spring – The Spring framework provides an abstraction of transaction management services and constructs. GigaSpaces has embraced this abstraction and uses it as the façade for its own transaction support.

To this mix GigaSpaces adds support for local transactions, meaning transactions that involve only one instance of one space.

Transaction Control

Mirroring Spring’s capabilities, GigaSpaces offers two modes of transaction control, programmatic and declarative. With programmatic transaction control, the programmer uses API calls to configure, start, commit and abort transactions. With declarative transaction control, the programmer includes directives about where and how transactional behaviour should be applied. These directives are interpreted by Spring and translated into transactional control statements that are woven into the application at runtime.

Spring declarative transaction control itself takes two forms, both of which are supported by GigaSpaces. First, it can be configured using a pointcut specification typical of aspect-oriented implementations. Second, methods can be annotated to indicate that they should (or should not) be executed within transactions.

A Simple Example

Here is a simple application that illustrates how to implement a local transaction with GigaSpaces using annotation-driven declarative transaction control. The example creates two instances of a class, then writes both instances to a space within a transaction.

There are five files:

1. A spring application context file – GSSimpleTranExample.xml – that sets up the proxy by which the application will access the space. The transaction manager is defined here.

2. A simple POJO class – TestClass.java – two instances of which will be written to the space under a transaction.

3. A Java interface – ConnBeanInterface.java – that declares the methods that will be used to access the space. The interface – implementation pattern is used because Spring works better with instances of interfaces than with instances of concrete classes.

4. A Java bean – ConnBean.java – that performs the space operations.

5. A Java main program – GSSimpleTranExample.java – that instantiates the objects to be written to the space and invokes the method to write them.

Let’s start by looking at the Spring application context file. The first item of interest is an Spring namespace element that instructs Spring to apply transactional controls to methods that are annotated with @Transactional in beans that it is managing:

<tx:annotation-driven />

The name of a transaction manager bean can be specified as an attribute to this element. This is the bean containing the transaction manager that will be used to manage the transactional behaviour of the annotated methods that Spring finds in the beans that it manages. If none is specified, as in our example, a default value of “transactionManager” is assumed.

Next is an OpenSpaces namespace element that instruct Spring to instantiate a transaction manager:

<os-core:local-tx-manager id=“transactionManager” space=“gSSimpleTranExample”/>

In our case we are using GigaSpaces’ local transaction manager. This is the best choice when each transaction will involve only a single partition of a single space.

Notice that the transaction manager declaration contains a reference to a space. As we will see shortly, this construct is one of two that constitute an apparent redundancy in the OpenSpaces namespace support for transactions.

Next we see a typical OpenSpaces namespace space declaration that tells Spring to create a an IJSpace instance:

<os-core:space id=“gSSimpleTranExample” url=“jini://*/*/GSSimpleTranExample” />

There is nothing specifically transactional about it; it is included in this discussion because the next element, which has a transactional dimension, refers to it.

Next is an OpenSpaces declaration of a GigaSpace:

<os-core:giga-space id=“gigaSpace” space=“gSSimpleTranExample”

tx-manager=“transactionManager” />

Note that this element includes both an explicit reference to the transaction manager declared earlier, and refers to the space that was defined earlier and that also refers to the transaction manager.

<os-core:giga-space id=“gigaSpace” space=“gSSimpleTranExample”

tx-manager=“transactionManager” />

Next we specify our application bean that will perform the space operations:

<bean id=“connBean” class=“ConnBean” />

Spring will instantiate this bean and manage its lifecycle.

We also include the OpenSpaces GigaSpace context element:

<os-core:giga-space-context />

so that Spring will assign the GigaSpace bean declared in the context file to a variable of type GigaSpace that is annotated with the @GigaSpaceContext annotation in our ConnBean instance.

The TestClass is not transaction-aware, and is not described further.

The ConnBeanInterface declares the method that will be implemented in ConnBean:

public interface ConnBeanInterface {

public void writeTwoObjects(TestClass tCI1, TestClass tCI2);

}

Note that, although it is not transaction-aware, it could have been, as we have the option of annotating the interface class or its methods to be transactional.

The implementation of:

public void writeTwoObjects(TestClass tCI1, TestClass tCI2) {

in our ConnBean class is transactional:

@Transactional

public void writeTwoObjects(TestClass tCI1, TestClass tCI2) {

gigaSpace.write(tCI1);

gigaSpace.write(tCI2);

}

The @Transactional annotation tells Spring to wrap transactional controls around this method.

From our main application class, here is the code that creates two instances of TestClass, then invokes the ConnBean method that will write them to the space under a transaction:

TestClass tCI1 = new TestClass(0);

TestClass tCI2 = new TestClass(1);

connBean.writeTwoObjects(tCI1, tCI2);

Running the Example

Before running this example, create an unpartitioned space called GSSimpleTranExample.

When you run the example pass the path and name of the spring application context file as a command line argument.

It it runs successfully the program will produce the following output:

Done with my work. About to exit.

Proving the Transactional Behaviour

Here are a few techniques that can be used to explore the transactional behaviour in this example:

1. Extend the duration of the transaction and inspect it in the GigaSpaces GUI Space Browser while it is in progress. You can extend the duration of the transaction by modifying the ConnBean class as follows:

int sleepLength = 10000;

@Transactional

public void writeTwoObjects(TestClass tCI1, TestClass tCI2) {

gigaSpace.write(tCI1);

gigaSpace.write(tCI2);

try {

Thread.sleep(sleepLength);

} catch (Exception e) {

// TODO Auto-generated catch block

e.printStackTrace();

}

}

sleepLength is specified in milliseconds. Set it to whatever value is convenient for you. Then run the application, and look at the list of transactions. Note that the transaction type is “Local” because we declared a local transaction manager in the application context file. Also notice that two objects are locked by this transaction. These are the two objects that are being inserted.

2. Try to query the locked objects using the Space Browser and observe that they cannot be read while the transaction is in progress. To do this, start by removing any instances of TestClass from the space. Set the value of sleepLength to a long enough duration (perhaps 30 seconds) that you will have time to execute a query against the space while the transaction is in progress. Run the program. Then select the TestClass class in the space browser and execute a query. The result set will be empty.

As you are preparing to run the query you will notice that the instance count for the TestClass class is two, not zero. This is because the method that is used by the GUI to inspect the space has access to locked objects and includes them in the count value it returns. The objects themselves, however, are not visible until the transaction commits.

3. Force the transaction to roll back, and observe that the space is left empty. To force a roll-back, raise an exception in the WriteTwoObjects() method as follows:

int sleepLength = 10000;

@Transactional

public void writeTwoObjects(TestClass tCI1, TestClass tCI2) {

gigaSpace.write(tCI1);

gigaSpace.write(tCI2);

try {

Thread.sleep(sleepLength);

} catch (Exception e) {

// TODO Auto-generated catch block

e.printStackTrace();

}

throw (new RuntimeException());

}

Again, start by removing any instances of TestClass from the space. Now when you run the application you see the transaction in progress and the TestClass instance count will go to two. At the end of the period specified by sleepLength, the transaction will abort and the instance count will revert to zero.

This DICE study I have been working on is as much about scalability – what happens to throughput when you add an additional unit of compute power – as about raw performance.  Nevertheless, testing eight different solutions to the same problem did provide some insight into overall performance characteristics.  I’ll tell you how the eight approaches stacked up in terms of speed, but first let me summarize the problem and the eight solutions.

The problem was to update the objects (“counters”) in a shared data area.  Each update was represented by an object (“updaters”) containing a reference to the counter object to be updated.  The updater objects also had to be written to the shared data area.  High ratios of updaters to counters promoted contention for access to the counters, creating classic hot spots.

Three solutions used Terracotta. The first had several instances of a simple program that processed a list of updaters.  The instances tussled for access to the counters.  The second solution was like the first except that each instance of the program had a list of updaters that referred to a distinct subset of the counters.  With this approach there was no competition for the Counters.  The third Terracotta solution had each instance of an updating program fed by a private queue.  Each queue contained updaters that referred to a distinct set of counters so, again, there was no competition for the counters.

Among these three approaches, the second proved to be the fastest overall, achieving over 5,000 updates per second when run with two or four instances of the updating program.  The first (and simplest) approach was the second fastest.  It’s best performance came with only a single updating program running, when it achieved over 3,400 updates per second.  Overall throughput declined precipitously as additional instances were added.

The third approach was the slowest, but got faster consistently as the number of instances was increased from one to two to four to eight (about the limit of my test environment).  With one instance of  the updating program running throughput was 576 updater per second.  At eight updaters throughput was 1,282 updates per second.

The five GigaSpaces solutions worked as follows:

1. A simple non-PU client that connects to a partitioned space and executes reads  and writes against the space.

2. Clients send updater objects to a partitioned space to be processed.  Updates performed by PUs against local space instances (space-based architecture) that use  a polling containers to detect the arrival of new updater objects.  Clients use writeMultiple() to improve throughput.

3.  Just like no. 2, but using FIFO features to preserve ordering of updates per counter.

4. Clients invoke remote methods advertised by PUs to update counters.  Updaters are passed as arguments.  PUs do work against local space instances.

5. Clients send update requests to spaces as Task objects.  Spaces execute the tasks.

Among these five approached, numbers two and three, both of which use writeMultiple() and polling containers, were the fastest by substantial margins.  Number two delivered over 30,000 updates per second with two clients and four updaters.  Number three came close to 17,000 updates per second with one client and two updaters.

Next fastest was number five at about 3,800 updates per second with one client and two updaters.  Number four peaked at around 2,400 updaters per second with two clients and two updaters.  Slowest of the five was number one, which reached between 1,000 and 1,100 updates per second in a variety of configurations.

Analyzing these results and explaining the differences in performance are topics too large for this post.  A few things are clear  however,  from even the simple set of results presented above:

1. The concept of locality – which decomposes into the related concepts of proximity and exclusivity – is profoundly important in designing solutions to this class of problem.

2. A very wide range of results is possible depending on the solution architecture.

3. For raw speed, GigaSpaces’ polling container construct offers a significant advantage over any of the other choices examined here.

I’ve done some quick performance testing of GigaSpaces’ distributed transactions using their mahalo implementation.  These are GigaSpaces-only transactions as opposed to transactions involving GigaSpaces and some other persistent store, in which case JTA/XA would be required.

As a reminder, GigaSpaces considers a transaction to be distributed if it involves more than one primary space partition.  So a transaction that operates on two or more partitions of a partitioned space would be distributed, as would a transaction that operates on two or more different spaces.  A transaction that operates on only one partition of one space is not distributed even if that space is replicated.

I set my test up as follows:

• A non-pu client acquires a proxy to a (remote of course) clustered space and writes pojos to that clustered space.

• The writes are single-threaded, one-at-a-time, and synchronous. (GigaSpaces offers other choices that would undoubtedly be faster).

• The pojos are routed, so the writes end up going to more than one partition.

I ran two GSCs on two virtual hosts.  When I ran without backups each GSC managed one partition.  When I ran with backups each GSC managed two partitions.  The primary for each partition ran on adifferent host than the backup when backups were used.

The client ran on the physical host.

Ping times on my network run at about .19 ms.

Each test consists of 10,000 operations of two writes (to primaries) each.  When I ran without backups and without transactions, I got 2,000 operations per second.  Using transactions that dropped to 322 operations per second.

Working with backups and without transactions,  I got 714 operations per second.  Using transactions that dropped to 208 operations per second.

These performance figures have little to do with fully optimized GigaSpaces performance.  As I mentioned above, there are faster ways to do these writes than the simple approach I used for these tests.  What the results do indicate, however, is that you can expect to pay a 3x – 6x performance cost for using distributed transactions over independent writes.

Of course transactions have different characteristics than do independent writes, and those characteristics may justify the performance cost.  As a rule, though, it is clear that distributed transactions should be avoided because of their performance implications unless you have a compelling need for transactional behaviour.

For weeks I’ve been working on a comparison of techniques for updating a distributed data set using Terracotta and GigaSpaces.   This weekend I finally got a draft out for review.  It is mostly ok,  but I got results that I can’t explain when I tested two of the GigaSpaces implementations (there are eight implementations in total – three with Terracotta and five with GigaSpaces).  I’ve asked my friends at the two vendor organizations to take a look at the draft and give me their comments.  Maybe my GigaSpaces contact can help me resolve those two mysteries.  Either way the paper is about done and I’m starting to move on to some new work.

Next on the agenda:

• I have spent some time fiddling with transactional techniques with GigaSpaces.  I’m planning to take that work a bit further and write up my results.

• GemFire has been on my list of products to investigate for awhile.  As of today I have it installed on my test network and I expect to start working with it next week.

Let me know if you are interested in the DICE paper or the GigaSpaces transactional work.