Monday Nov 10, 2014
Tuesday Apr 29, 2014
By Ricardo Ferreira-Oracle on Apr 29, 2014
The result cache is a very cool functionality introduced in Oracle Service Bus to allow ESB developers to automatically cache responses from a external service in OSB's built-in in-memory data grid caching system, which is Oracle Coherence. No matter which external service you are planning dealing with, an web service, an REST API, an directory in the file system or an CICS transaction, if the result cache functionality is activated for that external service, the response payload of an specific request message will be putted in the caching system for future reuse if the same request message is received again. The result cache functionality also allows you to define a expiration criteria, so the response payloads entries can eventually expire.
ESB developers will activate this functionality in OSB neither to protect critical back-end external services, to offload it or to short its response time. In the scenario that wants to protect back-end external services, perhaps those services have some cost associate every time you send a message for them. This cost would have various meanings, like per-request-basis (a paid external service that allows customer's credit history querying), IT budget (an CICS transaction service in which each call consumes MIPS) or even performance costs. In the case of performance costs is that when we start thinking in offloading. When services are originally designed, we measure some approximate throughput and average latency, and we put enough hardware resources to sustain that measure. When a ESB is situated in front of those services, you are enabling more channels to interact with that service and maybe the new amount of channels can be too high for the existing hardware resources. Finally, you could enable this functionality to short the response time of some services. If some services are sensitive in terms of response time latency, so the result cache is a must have.
A common practice used by customers around the world is to have replicas of their system architecture in different data centers, allowing them to survive in case of catastrophes. But only having a replica of their system architecture in different data centers is not enough. There is a need to provide business continuity, which means that every single detail of the system architecture should be constantly synchronized between the data centers, so when a backup data center take place in a catastrophe scenario, the down time should be minimal. There is also scenarios when even small periods of down time are not acceptable. All the data centers should be in stand-by/active mode to take over the entire processing in any moment. The challenge here is to keep two types of things synchronized: system architecture artifacts and system transactions. System architecture artifacts are any piece of data that the run-time system architecture needs to properly work. Common examples of artifacts are XML configuration files, applications, log files, data files and storage. System transactions are a unit-of-work of a business transaction. A business transaction represents a single or multiple business processes of the organization, and most of the times a business transaction are associated to a monetary need. E-commerce sites for instance are good examples of business transactions that are associated to a monetary need. If the site loses a single transaction, that lost represents less incoming money. And that is a situation that no CFO/CEO likes to tolerate.
Back to the result cache functionality, imagine that you have OSB deployed in two or more data centers operating in active-active mode. A corporate load balancer distributes load across each data center though its exposed services. When a request arrives in one data center, OSB take that request and start processing it, causing one or more entries to be stored in the result cache for future reuse. If the same request arrives in another data center, the desire is that OSB pick the already processed result from the result cache instead of processing it again. This is true because from the customer/client point of view, it is the same service and invocation request. But what will really happen is that the request will be processed again since result cache by default do not replicate entries across data centers, only across clusters in the same local network. So the challenge here is to find a way to enable entries being replicated from one local network (a.k.a "LAN") to a remote network (a.k.a, "WAN") even if this remote network is geographically distant.
In this article, I will show step by step how to enable result cache data replication across different data centers connected through a WAN. Thanks to OSB's great product architecture, this configuration is very straightforward and you will not have to change nothing in your SOA services, neither even in the OSB deployment. Everything is done out-of-the-box by Oracle Coherence. This article will help you even if WAN replication is not your primary objective. If you have different OSB domains (in the same or different networks) in which some services are exactly the same in those domains, the same technique should apply. All the examples created in this article were based on Oracle Service Bus 11gR1 default installation, which comprises WebLogic 10.3.6, Coherence 188.8.131.52 and Service Bus 184.108.40.206.
Patching Oracle Coherence from Middleware's Home
Before starting using the Push Replication Pattern feature available in Coherence Incubator (it will be explained in the next topic) we need to patch the Coherence installation that come with WebLogic. When you install the WebLogic pre-requisite for OSB which is the WebLogic 11gR1 + Coherence package installer, the Coherence 220.127.116.11 version is installed in the middleware home location. We need to patch this Coherence installation so we can take advantage of the latest features of the Push Replication Pattern.
Update Coherence to the 18.104.22.168 version. You can get access to this version in the Oracle Support website. After logged in the Oracle Support Self-Service portal, go to the "Patches and Updates" tab and search for the following patch number: 17897749. Download this patch and update the Coherence installation according to the instructions available inside of the patch file.
Installing the Oracle Coherence Push Replication Pattern
The Push Replication Pattern is a extension for the Oracle Coherence product to allow remote clusters to exchange data across WAN networks. It is part of the Coherence Incubator project, an very cool initiative to enhance the Coherence product through community based feedback. It hosts a collection of projects with implementations of real world needs, in a form of design patterns. Even being open in terms of source code access, it is responsibility of Oracle engineers to provide new features, correction of bugs and documentation.
You need to download a compatible version of Coherence Incubator to the Coherence 22.214.171.124 release. Use the following link to get instructions about how to download the source code. After downloading the source code, you need to compile and build the run-time packages. To accomplish that, you will need the Apache Maven project management tool. With Apache Maven properly installed, follow the instructions of this link to compile and build the Coherence Incubator run-time packages.
Setting Up a Coherence Cluster with WAN Replication Support
Let's set up a Coherence cluster that allows data replication across a WAN network. The first thing to do is the definition of cache configuration files for both sites. The idea for those cache configuration files is that it should contains definitions for publishing and receiving endpoints. That means that one site should expose one or more endpoints to receive events from the other site and also define a remove invocation service to connect to the other site to publish events. It is a bi-directional communication across the sites in which the Push Replication Pattern takes care about when to publish/receive events using the endpoints. The listing code below shows the cache configuration file for site-01:
Save this cache configuration file as coherence-cache-config-site-01.xml. Before we continue, let's spend some time understanding the code. If you look at the top of the configuration file you will see the mapping for the cache /osb/services/ResultCache. This cache name matches with the one the come bundled with OSB. Also in the cache mapping, you will see a section that starts with the tag event:distributor. This XML tag is part of the Coherence Incubator implementation as you probably have seen in the namespaces declaration section. The event:distributor section basically states for declaring which remote sites should receive events from created, modified, removed or expired entries of the local cache. In the declaration, it is defined that the site-02 will be updated through a remote invocation service declared as site-02-sync-proxy-service later in the configuration file.
Special attemption for the event:conflict-resolver-scheme section. This should be used when you are expecting that entries from one site conflicts with entries of another site, most of the time because synchronization failures due unstable network links. Using this section, you can plug custom implementations that would decide which entry should be considered. The BruteForceConflictResolver class used in this example is a out-of-the-box implementation that came with the Event Distribution Pattern, another pattern that is part of the Coherence Incubator project.
Finally, you also have two proxy-scheme declarations in the configuration file. The purpose of the site-01-trans-proxy-service is for receiving local events from the same site. As for the site-01-sync-proxy-service, it is used to receive remote events from the foreign sites. Using two different proxies, one for transaction and another for synchronization gives you the ability to fine tune each proxy throughput independently, configuring for instance a different pool of threads for each one. In theory, you should balance the same number of threads for both proxies to ensure a well synchronized cluster. The Push Replication Pattern executes its synchronization job between sites completely asynchronous, meaning that the thread that updates the local cache does not have to wait the thread the replicates the entry for a remote site. That is the reason why is so important have different proxies.
Now let's create the cache configuration file for the site-02. The listing code below is almost identical to the previous listing, except from the fact that this time we are defining how site-02 will synchronize with site-01:
Save this cache configuration file as coherence-cache-config-site-02.xml. Now that we have cache configuration files from both sites in place, we can set up the Coherence cluster that will hold the WAN replication enabled caches. For the site-01, create one shell script file named coherence-cache-server-site-01.sh and write the following code:
The given shell script code is self explanatory, so I will not enter in too much details. Just keep in mind that this type of cluster was designed to scale out, so if you need more storage capacity in the Coherence layer, just raise up more JVM nodes with the same configuration. Since there are no cluster defined, each JVM node that come up with will join the cluster automatically. Also, adjust the minimum and maximum heap sizes accordingly to suit your needs. Not to mention that you will need to adjust the global variables to your specific path needs.
For the site-02, create one shell script file named coherence-cache-server-site-02.sh and write the following code:
Execute each script on its respective site. Keep they up and running while we start the configuration of how each local OSB will connect to those clusters to delegate its caching needs.
Changing Oracle Service Bus Default Caching Configuration
The last part of the configuration is both the most simple and important one. We need to teach OSB about how to connect to a external cluster (created and configured in the previous topic) instead of using its built-in Coherence cluster. Let's start with the site-01. Edit the internal Coherence cache configuration file used by OSB located in the following folder: <DOMAIN_HOME>/config/osb/coherence/osb-coherence-cache-config.xml. You will need to change the contents of the original file with the contents of the following list below:
Let's understand what is being done here. Internally, OSB was built to invoke a cache named /osb/services/ResultCache when the result cache functionality is activated for a business service. Since we have changed its caching scheme, now when the cache is accessed, it will trigger remote invocations over TCP to the distributed cache available in the 20001 port. With the usage of a near-scheme type of cache, OSB can benefit from the best of worlds: part of the most recently data stored on its heap for rapid retrieval and the other part stored in a remote distributed cache. This configuration provides both high performance and scalability with the plus of easy administration, since all the data is stored in a cluster separated of OSB.
Here is the OSB cache configuration file for site-02:
As you can see, it is the same code with the same techniques. The only difference is that instead of pointing to the Coherence cluster of site-01 on port 20001, it points to the Coherence cluster of site-02 on port 20002. That's all what we need to have OSB delegating its caching needs to a remote cluster. The diagram below gives you an overview of what we have done so far.
Start OSB in both sites. During start up, OSB will connect to the Coherence cluster and establish a connection. Because of this, consider as a deployment procedure start first the Coherence cluster to after the OSB cluster. Now that we have WAN replication properly configured, let's start some tests.
Testing the WAN Replication Behavior in Oracle Service Bus
In order to test the WAN replication behavior, I have developed a simple web service which takes ten seconds to complete each request. The idea is to have this web service as a OSB business service with result cache activated. Then, you need to create a proxy service in which its only job is to route its requests to the business service. Both the proxy service and the business service should be deployed at all the sites, along with the Web Service deployment. Here is the snippet code from the web service implementation:
A simple battery of tests to validate if everything is working should be:
- Using the proxy service from site-01, make a request with "123456789" as the value of the SSN parameter. That request should take ~10 seconds to complete.
- Using the proxy service from site-02, make a request with "123456789" as the value of the SSN parameter. That request should take ~01 second or less to complete.
- Using the proxy service from site-02, make a request with "987654321" as the value of the SSN parameter. That request should take ~10 seconds to complete.
- Using the proxy service from site-01, make a request with "987654321" as the value of the SSN parameter. That request should take ~01 second or less to complete.
- Using the proxy service from site-01, make a request with "111111111" as the value of the SSN. Wait for the expiration of that entry in site-01. When it expires, check in the site-02 if the entry also expired.
Thinking in making things easier for you, I have made available all the project artifacts and OSB projects. Click in the links below to download them.
Monday Oct 28, 2013
By Ricardo Ferreira-Oracle on Oct 28, 2013
Cluster Topology and Configuration
In order to create an good didactic for the article, let's assume a cluster topology and configuration. In this example we have a six member cluster, consisting of one JVM on each physical machine. The member IDs are as follows:
|Member ID||IP Address|
Members 1, 2, and 3 are connected to a switch, and members 4, 5, and 6 are connected to a second switch. There is a link between the two switches, which provides network connectivity between all of the machines.
Member 1 is the first member to join this cluster, thus making it the senior member. Member 6 is the last member to join this cluster. Here is a log snippet from Member 6 showing the complete member set:
At approximately 15:30, the connection between the two switches is severed:
Thirty seconds later (the default packet timeout in development mode) the logs indicate communication failures across the cluster. In this example, the communication failure was caused by a network failure. In a production setting, this type of communication failure can have many root causes, including (but not limited to) network failures, excessive GC, high CPU utilization, swapping/virtual memory, and exceeding maximum network bandwidth. In addition, this type of failure is not necessarily indicative of a split brain. Any communication failure will be logged in this fashion. Member 2 logs a communication failure with Member 5:
The Coherence clustering protocol (TCMP) is a reliable transport mechanism built on UDP. In order for the protocol to be reliable, it requires an acknowledgement (ACK) for each packet delivered. If a packet fails to be acknowledged within the configured timeout period, the Coherence cluster member will log a packet timeout (as seen in the log message above). When this occurs, the cluster member will consult with other members to determine who is at fault for the communication failure. If the witness members agree that the suspect member is at fault, the suspect is removed from the cluster. If the witnesses unanimously disagree, the accuser is removed. This process is known as the witness protocol. Since Member 2 cannot communicate with Member 5, it selects two witnesses (Members 1 and 4) to determine if the communication issue is with Member 5 or with itself (Member 2). However, Member 4 is on the switch that is no longer accessible by Members 1, 2 and 3; thus a packet timeout for member 4 is recorded as well:
Member 1 has the ability to confirm the departure of member 4, however Member 6 cannot as it is also inaccessible. At the same time, Member 3 sends a request to remove Member 6, which is followed by a report from Member 3 indicating that Member 6 has departed the cluster:
The log for Member 3 determines how Member 6 departed the cluster:
In this case, Member 3 happened to select two witnesses that it still had connectivity with (Members 1 and 2) thus resulting in a simple decision to remove Member 6.
Given the departure of Member 6, Member 2 is left with a single witness to confirm the departure of Member 4:
In the meantime, Member 4 logs a missing heartbeat from the senior member. This message is also logged on Members 5 and 6.
Next, Member 4 logs a TcpRing failure with Member 2, thus resulting in the termination of Member 2:
For quick process termination detection, Oracle Coherence utilizes a feature called TcpRing which is a sparse collection of TCP/IP-based connections between different members in the cluster. Each member in the cluster is connected to at least one other member, which (if at all possible) is running on a different physical box. This connection is not used for any data transfer, only heartbeat communications are sent once a second per each link. If a certain number of exceptions are thrown while trying to re-establish a connection, the member throwing the exceptions is removed from the cluster. Member 5 logs a packet timeout with Member 3 and cites witnesses Members 4 and 6:
Eventually we are left with two distinct clusters consisting of Members 1, 2, 3 and Members 4, 5, 6, respectively. In the latter cluster, Member 4 is promoted to senior member.
The connection between the two switches is restored at 15:33. Upon the restoration of the connection, the cluster members immediately receive cluster heartbeats from the two senior members. In the case of Members 1, 2, and 3, the following is logged:
Likewise for Members 4, 5, and 6:
This message indicates that a senior heartbeat is being received from members that were previously removed from the cluster, in other words, something that should not be possible. For this reason, the recipients of these messages will initially ignore them. After several iterations of these messages, the existence of multiple clusters is acknowledged, thus triggering the panic protocol to reconcile this situation. When the presence of more than one cluster (i.e. Split-Brain) is detected by a Coherence member, the panic protocol is invoked in order to resolve the conflicting clusters and consolidate into a single cluster. The protocol consists of the removal of smaller clusters until there is one cluster remaining. In the case of equal size clusters, the one with the older Senior Member will survive. Member 1, being the oldest member, initiates the protocol:
Member 3 receives the panic:
Member 4, the senior member of the younger cluster, receives the kill message from Member 3:
In turn, Member 4 requests the departure of its junior members 5 and 6:
Once Members 4, 5, and 6 restart, they rejoin the original cluster with senior member 1. The log below is from Member 4. Note that it receives a different member id when it rejoins the cluster.
Cool isn't it?
Friday Aug 16, 2013
The Perfect Marriage: Oracle Business Rules & Coherence In-Memory Data Grid. High Scalable Business Rules with Extreme Low Latency
By Ricardo Ferreira-Oracle on Aug 16, 2013
The idea of separating business rules from the application logic is by far an old concept. But in the last ten years, what we have seem is that dozen of platforms and technologies has been created to allow this separation of concerns. One of those technologies is BRMS, acronym of Business Rules Management System. The basic idea of one BRMS is to be a repository of rules, governing those rules in such way that they can be created, updated, tested and controlled by an external interface. Part of the BRMS responsibility it is also provide an API (more than one when possible) that allows external applications to interact with the BRMS, allowing those applications to send data over the network, and that data can trigger the execution of zero, one or multiples rules in the BRMS repository. This rule execution occurs outside of those external applications, minimizing their process memory footprint and generating much less CPU overhead since the execution processing of the rules happens in a separated server/cluster. This architecture approach is very powerful, allowing:
- Rules can be managed (created, updated) outside of the application code
- Rules can be reused across different applications, no matter their technology
- Less CPU overhead and smaller memory footprint in the applications
- More control over rules, auditing of changes and enterprise log history
- Integration with other IT artifacts like dictionaries, processes, services
With this context in place, we are all agree that the usage of one BRMS is a mandatory approach on every IT architecture due its power, if it were not for the fact that BRMS technologies introduces a lot of overhead in the overall transaction latency. In the middle of the external application that invokes the BRMS to execute rules and the BRMS platform itself, there is the network channel. This means that we must deal with network I/O and their technical implications (serialization, instability, buffering bytes approach) when we send/receive data to/from the BRMS. No matter if the BRMS provides an SOAP API, an REST API or any other TCP/IP based API, the overall transaction latency is compromised by the network overhead.
Another huge problem of BRMS platforms is scalability. When the BRMS platform is first introduced to an architecture, it handles an acceptable number of TPS (Transactions Per Second), which nowadays varies from 1K TPS to 5K TPS. But when other applications starts using the same BRMS platform, or the number of transactions just naturally grows, you can face scenarios when your BRMS platform must deal with 20K TPS or even 100K TPS. What happens when a huge numbers of objects are allocated in the heap space of the Java based server? The memory footprint starts to reach its maximum size and the garbage collector starts to run to reclaim the unused memory and/or redesign the layout space. No matter what job the garbage collector has to do, it will use the entire processing power to runs its job as soon as possible, since the amount of garbage to handle will be huge. This is true for the almost BRMS platforms of the market, no matter if its from one vendor or another. If the BRMS platform are Java based, when those servers JVM reach more than 16 GB of space in average, they starts to face a huge performance problem due garbage collection.
Differently from other architecture designs in which the load is distributed across a cluster, BRMS platforms must handle the entire processing in a single server due a general concept of BRMS platforms known as execution agenda and working memory. All the facts (the data sent as input) are maintained in this agenda in a single server, making the BRMS platform a pinned service, in which they do their job in a singleton fashion. In this situation, when you need to scale, you can introduce series of equally servers, below a corporate load-balancer that instead of distribute load, it divides entire transaction volumes across those servers. Because each server below the load-balancer handle the entire volume by itself, those servers limit concurrency by the number of processors available in their mainboard. If you need more compute power, due lack of concurrency, you are forced to buy a much higher server. Those servers are huge, expensive and costs a lot of money since they need to be big enough in terms of processors to handle thousands of executions simultaneously and completely alone. Not a very smart approach when you considering to handle millions of TPS.
With this situation in mind, it is necessary to design an architecture that would allow business rules execution be distributed across different servers. To achieve this behavior, it is necessary to use another software component that could share data (business entities, fact types, data transfer objects) across different processes, running in the same or different hardware boxes. And more important than that, a software component that would allow transaction latency to be short enough, reducing a lot of milliseconds introduced by network overhead. In other words, this software component must bring data to the unique hardware layer that really doesn't implies in I/O overhead, which is memory.
Recently, in order to deal with this problem and provide for a customer an scalable plus high performance way to use Oracle Business Rules, I designed an solution that solves both problems in a once, without losing the power of separation of concerns provided by BRMS platforms. In-Memory Data Grid technologies like Oracle Coherence has the power of handling massive amounts of data (MB, GB or even TB) completely in-memory. Moreover, this kind of technology has been written from scratch to distribute data across a number of servers, so scalability is never a problem here. When you integrate BRMS with In-Memory Data Grid technologies, you can do both of the two worlds: scalability plus high performance and also extreme low latency. And when I say extreme low latency I mean, sub-milliseconds of latency. Something around less than 650 μs in my tests.
This article will show how to integrate Oracle Business Rules with Oracle Coherence. The steps showed here can be reproduced for a huge number of scenarios, making your investment on Oracle Fusion Middleware (Cloud Application Foundation and/or SOA Suite stack) even more attractive.
The Business Scenario: Automatic Promotions for Bank Customers
Before we move to the implementation details of this article, we need to understand the business scenario used as didactic. We are about to simulate an automatic decision system that create promotions for banking customers based on their profiles. The idea here is let the BRMS platform decide which promotions to offer based on customer profiles that applications send it. This automatic promotion system should allow applications like internet banking sites, mobile applications or kiosk terminals, to present promotions (up-selling/cross-selling) to its final customers.
Building the Solution Domain Model
Let's start the development of the example. The first thing to do is the creation of the domain model, which means that we need to design and implement the business entities that will drive the client-side application execution, as such the business rules. The automatic promotion system will be composed of three entities: promotions, products and customers. A promotion it is something that the bank would offer to the customer, with contextual information about the business value of one or more products, derived from the customer profile. Here is the implementation of the promotion entity:
A product is something that the customer hire from the bank. Some kind of service or item that make the customer account more valuable to the bank and more attractive to the customer since it is a differentiator. Here is the implementation of the product entity:
And finally, we need to design the customer entity. The customer entity will be the representation of the person or company that hires one or more products from the bank. Here is the implementation of the customer entity:
As you can see in the code, the customer entity has a relationship with the two other entities. Build this code and package those three entities into a JAR file. We can now move to the second part of the implementation which is the creation of one SOA project that includes an business rules dictionary.
Creating the Business Rules Dictionary
Business rules in the Oracle Business Rules product are defined in an artifact called dictionary. In order to create an dictionary, you must use the Oracle JDeveloper IDE plus the SOA extension for JDeveloper. I will assume here that you are familiar with those tools, so I will not enter in too much detail about them. In JDeveloper, create a new SOA project, and after that create a business rules dictionary. With the dictionary in place, you must configure the dictionary to consider our domain model as fact types.
Now you can write down some business rules. Using the JDeveloper business rules editor, define the following rules as shown in the picture below.
For testing purposes, the variable "MinimumBalanceForCreditCard" it is just a global variable of type java.lang.Double that contains a constant value. Finally, you are required to expose those business rules through an decision function. As you probably already know, decision functions are constructions that make easier external applications to interact with Oracle Business Rules, minimizing the developers effort to deal with the Oracle Business Rules API, besides providing a very nice contract-based access point. Create one decision point that receives an customer as input, and returns the same customer as output. Don't forget to associate the ruleset with the decision function.
Integrating Oracle Business Rules and Coherence through Interceptors
Now here came the most exciting part of the article: the integration between Oracle Business Rules and Oracle Coherence In-Memory Data Grid. Starting from 12.1.2 version of Coherence, Oracle announced an new API called Live Events. This new API allows applications to listen/consume events from Coherence, no matter what type of event it is being generated. You can learn more about Coherence Live Events in this Youtube presentation.
Using both Coherence and Oracle Business Rules main libraries, implement the following event interceptor at your favorite Java development environment:
If you are familiar with the Oracle Business Rules Java API, you won't find any difficult to understand this code. What it does is simply create an DecisionPoint object during the constructor phase and put this object into a static variable, which allow this object to be shared across the entire JVM. Remember that the JVM in this context is a Coherence node, so what I am saying is that each Coherence node will hold an instance of one DecisionPoint. On the onEvent() method, there is the algorithm that checks which type of event the implementation should intercept, and also checks if the DecisionPoint instance should be updated. This last check is done based on the timestamp of the dictionary file.
After creating an DecisionPointInstance, the intercepted entries became the input variables for the business rules execution. The interceptor triggers the rules engine through the invoke() method, and after that it replaces the original intercepted entries with the result that came back from the business rules agenda. But only if one of the following events had happened: INSERTING or UPDATING. This check is necessary for two reasons. First, those are the only event types that occurs in the same thread of the cache transaction. Second, other event types like INSERTED or UPDATED happens in another thread, which means that they are triggered asynchronously by Coherence.
Setting Up an Coherence Distributed Cache with the Business Rules Interceptor
Now we can start the configuration of the Coherence cache. Since we are using POF as the serialization strategy, we need to assembly an POF configuration file. Starting from the 12.1.2 version of Coherence, there is a new tool called pof-config-gen that introspects JAR files searching for annotated classes with @Portable. Create a POF configuration file that should contain the following content:
And as expected, we also need to create an Coherence cache configuration file. Create one file called coherence-cache-config.xml and fill it with the following contents:
This cache configuration file is very straightforward. There is only three important things to consider here. First, we are using the new interceptor section to declare our interceptor and pass constructor arguments for it. Second, we used another feature from Coherence 12.1.2 version, which is the asynchronous backup feature. Using this feature dramatically reduces the latency of one single transaction, since backups are written after (in another thread) that the primary entry has been written. Not necessarily a pre-condition for the interceptor stuff works, but in the context of BRMS, should be a great idea. Third, we also defined a proxy-scheme that expose an TCP/IP endpoint, so we can use the Coherence*Extend feature later in this article, to allow a C++ application to access the same cache.
Testing the Scenario
Now that we have all the configuration in place, we can start the tests. Start an Coherence node JVM with the configuration file from the previous section. When you start the Coherence, a DecisionPoint object pointing to the business rules dictionary will be created in-memory. Implement a Java program to test the behavior of the implementation as the listing below:
This Java application can be executed with the storage-enabled parameter set to false. Executing this code will give you an output similar to this:
As you can see in the output, the number of promotions showed reveals that the business rules were really executed, since during the instantiation of the customer object promotions weren't provided. The output also tells us another important thing: transaction latency. For the first cache entry we got 53 ms as overall latency, quite short if you consider what happened behind the scenes. But the second cache entry is even much more faster, with 0 ms of latency. This means that the actual time necessary to execute the entire transaction was something below of one millisecond, giving us an real sub-millisecond latency scenario, measured in microseconds.
High Scalable Business Rules
It is not so obvious when you understand this implementation for first time, but another important aspect of this design is scalability. Since the cache type that we used was the distributed one, also known as partitioned, the overall cache entries are equally distributed among all Coherence nodes available. If we use only one node, of course that this one node will handle the entire dataset by itself. But if we use four nodes, each node will handle 25% of the dataset. This means that if we insert one million customer objects in the cache, each node will handle only 250K customers.
This type of data storage offers a huge benefit for Oracle Business Rules, which is the truly data load distribution. Remember that I said before that each Coherence node will hold one DecisionPoint instance? Since each node handle only a percentage of the entire dataset, its reasonable to think that each node will fire rules only for the data that it manages. This happens this way because Coherence interceptors are executed in the JVM that the data lives, not in the entire data grid since it is not a distributed processing. For instance, if the customer "A" is primarily stored in the "JVM 1", and this customer "A" has its fields updated by one client application, business rules will be fired and executed only in the "JVM 1". The other JVMs will not execute any business rules. This means that CPU overhead can be balanced across the cluster of servers, allowing the In-Memory Data Grid scale up horizontally, using the overall compute power of different servers available in the cluster.
API Transparency and Multiple Programming Language Support
Once the Oracle Business Rules is encapsulated in Coherence through an interceptor, there is another great advantage of this design: API transparency. Developers don't need to write custom code to interact with Oracle Business Rules. In fact, they don't ever need to know that business rules are being executed when objects are written in Coherence. Since all happens behind the scenes, this approach free developers from extra complexity, allowing them to work only in a data-oriented fashion which is very productive and less error prone.
And because Oracle Coherence offers you not only a Java API to interact with the In-Memory Data Grid, but also a C++, .NET and an REST API, you can leverage several types of clients and applications to trigger business rules executions. In fact, I have created a very small C++ application using Microsoft Visual Studio to test this behavior. The application code below inserts 1K customers into the In-Memory Data Grid, with an average transaction latency of ~5 ms, using a VM with 3 vCores and 10 GB of RAM.
An Alternative Version of the Interceptor for MDS Scenarios
The interceptor created in this article uses the Oracle Business Rules Java API to read the dictionary directly from the file system. This approach suggests two things: first, that the repository of the dictionary will be the file system. Second, that the authoring and management of the dictionary will be done through JDeveloper. This can lead into some lost of the BRMS power since business users won't feel comfortable authoring their rules in a technological environment such as JDeveloper. Administrators won't have the power of see who changed what since virtually any person can open the file in JDeveloper and change its contents.
A better way to manage this is storing the dictionary in a MDS repository, which is part of the Oracle SOA Suite platform. Storing the dictionary in the MDS repository allows business users to interact with business rules through the SOA composer, a very nice web tool, more simpler and easy-2-use than JDeveloper. Administrators can also track down changes, since everything in the MDS are audited, transaction based and securely controlled, since you have to first log in the console to get access to the composer.
I have implemented another version of the interceptor, making full use of the power of Oracle SOA Suite and MDS repositories. The implementation of MDSRulesInterceptor.java is being tested for over a month and is performing quite well, just like the FSRulesInterceptor.java implementation. In the future, I will post here this implementation, but for now just keep in mind the powerful things that can be done with Oracle Business Rules and Coherence In-Memory Data Grid. Oracle Fusion Middleware really rocks isn't?
Saturday Jul 13, 2013
Upgrading to Coherence for C++ 12.1.2 and The "Ambiguous Compilation Error" in Types Derived From AbstractAggregator
By Ricardo Ferreira-Oracle on Jul 13, 2013
This weekend, I started the migration of some old C++ applications built on top of Coherence for C++ 3.7.1 API to its newest and refreshed version, released a few days ago. Version 12.1.2 introduces a lot of cool changes and features in the Coherence product, not mentioning the improvements done in different areas like installation, WebLogic integration, TCMP, Exabus, REST and Coherence*Extend. If you got yourself interested about those changes and improvements, check out the documentation at the following link, and please join us for the live virtual launch event on July 31st. Registration here.
After a couple hours migrating my projects from the oldest version to the newest, I got surprised with the following compiler message when trying to build the code:
call of overloaded 'Float64Sum(const coherence::lang::TypedHandle<coherence::util::extractor::ReflectionExtractor &)' is ambiguous
If you are experiencing the same compiler message... don't worry. There is a quick and clean solution for this. This compiler error happens because in the 12.1.2 version of Coherence for C++ API, an overloaded version of the create() factory method was introduced in the types derived from the coherence::util::aggregator::AbstractAggregator class.
Until 3.7.1 version, the only way to instantiate an AbstractAggregator object was passing an instance of an coherence::util::ValueExtractor to its factory method. Now you also have the option to pass an instance of an coherence::lang::String::View. This object should contain the name of the attribute that will be aggregated. Automatically and behind the scenes, the Coherence for C++ API will fabricate an coherence::util::ValueExtractor for you.
In my case, I've changed my code from this:
Float64Sum::Handle scoreAggregator = Float64Sum::create(ReflectionExtractor::create("getScore"));
Float64Sum::Handle scoreAggregator = Float64Sum::create("getScore");
And my project was able to be completely compiled again, both using the GNU Linux G++ and MS Visual Studio for C++ compilers.
Hope this blog helps you eliminate some hours of code debugging :-)
Tuesday Mar 19, 2013
Creating Scalable Fast Data Applications using Oracle Event Processing Platform (Setting Up an Active-Active Oracle CEP Domain)
By Ricardo Ferreira-Oracle on Mar 19, 2013
This article will discover some technical aspects that should be considered if you are involved in serious implementations of Oracle CEP, the technical foundation of the Oracle's strategy for Fast Data called Oracle Event Processing Platform. It is expected that you have some basic knowledge about Oracle CEP, JMS and some knowledge about programming using Java.
Fast Data and the Concern with Scalability
There is no such thing of application not meant to grow. Every application, even the simpler ones should expect some growth across the months or years during the time they are up and running. Growing is a consequence of a lot of things such as organizational growth, application maturity which in turn gives users more confidence to use it, an marketing campaign that worked and brought much more clients than expected, more front-ends enabling people to interact with your application through other types of devices, exponential generation of data from social networks that your application is configured to listen to, an market opportunity that demands more of your software or perhaps just natural growth of the users installed base.
It doesn't matter the source of growing, your application need to be ready to scale up. And this is true no matter which architectural style you're considering it as your strategy. Of course, there are some architectural styles that suggests a moderated growth like the client-server style or maybe the monolithic style. But take for instance the SOA ("Service-Oriented Architecture") style. The basic concept behind this architectural style is the reuse of services, which are the building blocks of the functional architecture, representing the business knowledge (standards, culture, procedures, routines) of an organization in a form of reusable functions. As much the reuse growths, more scalable your SOA foundation must be. You virtually can't predict the level of reuse of your services, but the key thing is, you should design your services to really scale up.
Another great example of architectural style that need to be designed to scale up is EDA ("Event-Driven Architecture"), which basically deals with processing of heterogeneous events coming from different sources, with different message formats and more importantly, with event channels that could potentially generate a number of events with different throughput's, frequencies and volumes. In the SOA architectural style this could happens too of course, but the scariest thing about EDA is that you don't necessarily deals with fixed message schemas, neither with well known message contracts. The previous knowledge about message contracts and schemas gives you the ability to predict the message size that the hardware infrastructure must deal with, an important requirement when you are sizing an infrastructure based on reasonable levels of reuse, like in the case of SOA.
As mentioned earlier, in the EDA architectural style you cannot predict the message schema or contract of yours events. It can be virtually any message format containing both structured and/or unstructured content. An good event-driven solution must be able to deal with this kind of situation, making the task of sizing an ideal hardware infrastructure really tough. Sizing an ideal hardware for an event-driven solution is a combination of both science and imagination. There are a lot of things to consider, a lot of scenarios to evaluate, a lot of hardware and/or software failures to predict and a huge number of situations that could potentially stops your application to run due hardware resources limitation, even in the first five hours running in production. Believe me, it is really tough.
Designing an event-driven solution that are ready to scale up demands more from the regular architect role that we found nowadays. It requires deep knowledge of the problem domain, deep knowledge of distributed systems, deep knowledge of servers systems (and perhaps engineered systems), deep knowledge of enterprise integration patterns and deep knowledge of the software's stacks used to build the solution, regardless if it is a proprietary, open-source or a combination of both.
Why do you need to worry about scalability? Because ten years ago the market demanded for event-driven solutions prepared to handle hundred of events per second. Today is the time of building event-driven solutions that should handle thousands of events per second. Fast Data, one of the new buzzwords of IT, demands for event-driven solutions that should handle millions of thousands events per second. My advice for any architect responsible for an event-driven solution should be, thinking in scalability as a huge main goal just like the problem domain to be solved.
Business Scenario for Scalability Study on Oracle CEP
Let's start our study of applying scalability in EDA considering a business scenario. Imagine that you are designing a EDA solution that combines technologies like CEP, BAM and JMS to deliver near real-time business monitoring of KPIs ("Key Performance Indicators") for an financial services company. All the information needed to process the KPIs came from a JMS channel that must be listened by an specialized adapter. Those JMS messages will be the business events. Inside every business event, there are information about payment transactions, containing some data like the total amount paid, the credit card brand, etc. The idea here is to process those payment transactions as they happens, in order to generate valuable KPIs that could be monitored through a near real-time monitoring solution like a BAM. For simplicity reasons, let's consider only one KPI for instance, and concentrate our focus on the CEP layer that is responsible for the KPIs compilation and aggregation. The example KPI will be the total count of payment transactions per second.
In order to compute this KPI, the CEP layer must execute the count aggregation function onto the stream of events, considering only those events of the last second. This means that this KPI will be compiled and aggregated on every one second (1000 milliseconds of time window) and the output should be also generated on every one second. The EPN ("Event Processing Network") of this business scenario should be something simpler like this:
Reading this EPN is not that complicated. You must basically read the flow from the left to the right. The basic idea behind this EPN is: listen the business events from an JMS adapter, put those events sequentially based on their temporal order into a event channel, compute the KPI based on the stream of events using an processor, send the generated output event (the KPI itself) to an new event channel and finally, present the KPI into the server output console using a custom adapter.
The event model of this EPN is composed by two simple event types. The first event type is the concept of the payment transaction, which acts in the EPN as event source. This event type contains three fields: an dateTime field that tells you the exactly moment that the payment transaction occurred, an amount field that reveals the amount paid for a product and/or service, and an brand field that tells you which credit card type was used in the payment transaction. The second event type would be the transactions per second KPI, which acts in this EPN as complex event. The only field that this event type has is the totalCountTPS, which represents the computed value of this KPI. The following UML class diagram summarizes this event model.
All the payment transactions are received through an built-in JMS adapter called in the EPN of paymentTransactionsJMSAdapter. This adapter is configured to listen to an JMS destination through an dedicated connection factory. The listing below is the configuration file for this JMS adapter.
The processor that will compute the KPI is also very simple. It basically counts the events coming from the event channel, filtering only those events that make part of one whole second. It also filters those events that has some meaningful values in the amount and brand fields, to prevent the computation of the KPI based on dirty events. The following CQL ("Continuous Query Language") statement are used to compute the KPI:
Finally, let's take a look in the last part of the EPN, which is the custom adapter created to print the results of the TransactionsPerSecondKPI event type in the server output console. As I mentioned earlier, for simplicity reasons I will not show how this event will be monitored in a BAM. Instead, I have created a custom adapter using the Oracle CEP adapters API to print in the server output console the content of the totalCountTPS field present in the TransactionsPerSecondKPI event type. The listing below is the implementation in Java of this custom console adapter.
As you can see in the code, this custom adapter doesn't make anything special. It just access the totalCountTPS field from the TransactionsPerSecondKPI event type and print the value in the server output console. The idea here is just to monitor in near real-time the computation of the KPIs, in order to test the behavior of Oracle CEP when is running in a single JVM or when its running in clustered on multiple JVMs.
Creating a Testing Environment to Simulate the Payment Transactions
Now that we are all set regarding the business scenario, we can start the tests. You need to create a simple Oracle CEP domain to test this application. You will also need an JMS provider to host the JMS destination. I would recommend you to use Oracle WebLogic as the JMS provider, but feel free to use any JMS provider compliant with JMS 1.1. Setup your JMS provider and create an queue to be used in the tests, and setup your Oracle CEP JMS adapter to listen this queue. Later in this article I will discuss more about the difference of using queues and topics, but for now let's just focus on the functional testing.
Implement the following Java program in your development environment. The program is just an example about how to send the JMS messages to an queue, and it considers that you are connecting to an WebLogic JMS domain. If you prefer using another JMS provider, you should adapt this program to correctly connect to your host system.
What this program does is continuously send ten messages to the specified JMS queue. As you can see in the code, after sending the messages, it takes a pause of one second, considering the elapsed time taken to send the messages to the JMS queue. This program never ends, unless of course that the user terminate the JVM created.
Starting the Functional Tests with one Single Oracle CEP JVM
Make sure that all your development environment are up and running, including your JMS provider and an fully operational Oracle CEP domain. Deploy the Oracle CEP application into this domain and run the client JMS application. An recorded result of this functional test is published on Youtube, so you can check it out the results of this test.
In the following sections, it will be discovered two different approaches for applying scalability. These approaches will be applied in the scenario of near real-time business monitoring of KPIs, transforming it into a more scalable solution that could handle the growing of the number of events just adding more Oracle CEP JVMs across the same and/or multiple server systems.
The Simpler Scalability Approach Ever: Using JMS Queues
Being you just designing an application that should deal with asynchronous calls or, just worried about delivery guarantees of messages, use JMS queues is always a good choice. Consumer applications connected into a JMS queue works hard for the reading of the most recent message, in a FIFO style. This means that each consumer will enter in a race condition along with other consumers to compete about which one gets the maximum number of messages possible. For the application consumer perspective, this could be a hard problem since there are no guarantees that an specific message will be consumed by an specific consumer, but for the JMS queue perspective, this is a really powerful scalability technique. The reason for that is because each consumer application will work hard to provide maximum throughput for the consumption of messages.
Imagine for instance an consumer application connected to an JMS queue, running on a server system with 24 cores of CPU, being two hardware chips with 6 cores each, using the Hyper-Threading Intel technology. If we realize that this consumer application running on this type of hardware gives us an average throughput of 3,000 TPS ("Transactions per Second"), we can certainly assume that putting another copy of the same consumer application, running on another server system with the same hardware configuration will give us an average throughput of 6,000 TPS. This is what we call horizontally scalability, when you increase the throughput of an software based application adding more server systems that will engage its hardware resources to an specific software goal, which in this case is consume as fast as possible the messages from an JMS queue.
This type of scalability technique delivers another important advantage: when you increase the total count of server systems running the consumer applications, you minimize the number of messages that each server system will need to handle. This seems to be a little bit contradictory isn't it, since in theory should be "a good thing" that each server system handle more messages as possible. Well, it is a good thing, but like anything else in software architecture, there are trade-offs. Consider for instance each consumer application running on the same hardware configuration. In this scenario, is reasonable to think that each server system will handle the same number of messages in average, because each server system are putting its hardware resources to work equally, and due the nature of the JMS queues (FIFO style consumption), the total number of messages of the queue will be divided to the number of server systems available.
The problem here is the memory footprint of each JVM running the consumer application. If you are consuming messages from the JMS queue with fewer server systems, the total number of messages that each server system will need to handle will be higher. Since each received message is allocated in the heap memory of the JVM, the total size of the heap will increase. Did you know that an javax.jms.MapMessage with an payload of 256 bytes allocates more than 400 bytes in the heap space? Imagine all those messages being received by your consumer application in the same time. You could reach the maximum size of your heap in a question of seconds. And the problem of reaching the maximum size of an JVM is the inevitable execution of the garbage collector. When the JVM detects that the heap memory are full (or almost full depending of the algorithm used) or to much fragmented, it engages the garbage collector threads to reclaim the allocated memory and/or rearrange the heap layout space. Depending of the situation of the heap memory, the JVM could use almost all the CPU cores of the server system to accomplish this task, and that's when your consumer application starts to be slower than usual, presenting performance issues and becoming a system bottleneck.
Let's consider the usage of JMS queues in our business scenario of near real-time business monitoring of KPIs. Each Oracle CEP JVM would be connected to an JMS queue that would maintain the messages of payment transactions. Each Oracle CEP JVM would act as an consumer application through its JMS adapter. Considering that each Oracle CEP JVM (or a group of it) would be running on separated server system, we could assume that the increasing of the number of server systems will increase the average throughput of message consumption from the JMS queue, and also that each Oracle CEP JVM would handle a reasonable number of messages on its JVM heap. Enough of theory, let's in see in practice how this scalability approach could be applied in our business scenario.
The first thing to do is transform your Oracle CEP domain in a multi-server and clustered domain. You can find a comprehensive set of information in the product documentation to help you doing this, but I will highlight the main steps for you here.
In the root directory of your Oracle CEP domain, you will find a sub-directory called "defaultserver". As the name suggests, this is your default server that are created automatically during the domain creation. For development and staging environments, this server is more than enough. But if you want to expand your domain, will will need to change that. Rename the default server directory from "defaultserver" to "server1". After that, make two copies of this directory, and call these newly created directories of "server2" and "server3" respectively.
Now you have to setup up some aspects of the cluster. Open the configuration file of the server1 in a text editor. The configuration file of the server1 can be found in the following location: <DOMAIN_HOME>/server1/config/config.xml. With the configuration file opened in the text editor, you will have to change the "netio" and "cluster" tags. Change the port value of the "NetIO" component to "9001", and the port value of the element "sslNetIo" component to "9011".
In the cluster tag you will have to define how the server1 will behave together with other members. Edit the cluster tag according to the listing below:
That's it. This is what have to be done to transform a simple server in a clustered-aware member. Repeat the same steps to the server2 and to the server3. Just keep and mind that if you plan to execute those servers in the same server system, will need to define different ports for each server. Also remember that the tag "server-name" must match with the server name you gave, which would be the name of the directory of the server.
One of the advantages of using JMS queues as your scalability approach is that you don't need to change anything at your Oracle CEP application. Just deploy the same application in all the new servers and start the tests again. Start the servers server1 and server2 and run the client JMS application. You will see in the servers output that each server will be generating the totalCountTPS KPI based on the number of events that it could receive from the JMS queue, which would be the division of the total count of events (ten events per second) by the total count of servers, which in this case is two. This will result in five events per server in average. If you start the server3 during the processing of events, you will see that the total number of events that each server is handling will decrease, which is an evidence that the scalability is really working due the fact that the servers partitioned the load of events.
An recorded result of this second test are also available on Youtube. Watch out the video below for how using JMS queues affects the scalability of your Oracle CEP applications.
So far, it seems that using JMS queues as your scalability approach is the right thing to do right? In fact, for the most demanding scenarios, this approach should be enough. But you should be aware of one catch: in-flight events can be missed during failures. I mean "in-flight" for all those events that had been received by the JMS adapter and an acknowledge of this receiving has been sent back to the JMS provider. On that moment, the message are not longer in the JMS queue, and more importantly, no other Oracle CEP JVM is aware of this event. This means that there are no recovery of events during failures in this approach. If you cannot tolerate missing events, using JMS queues for scalability is not the most reliable approach. But if your scenario can tolerate missing events, don't think twice and choose this approach once is simpler and does not implies in changing the Oracle CEP application.
Reliability Really Matters: Complicating Things Just a Little Bit with JMS Topics
OK, so you realized that you cannot tolerate missing events, and you need to improve as much as possible the reliability of your Oracle CEP application. The road to achieve this is quite more longer than just using JMS queues. There are some challenges that you need to surpass in order to conquer this level of reliability when no missing events can be tolerated without of course putting scalability aside. The challenges that you will need to surpass are:
- Provide guarantees that all of the JVMs are aware of all events
- Equally distribute the load of events across all of the JVMs
- Ensure that even in-flight events are completely synchronized
- Provide backups for every cluster member to ensure HA
The good news is, all those challenges can be easily surpassed, and the Oracle CEP product provides native support for all that stuff. But the bad news is, compared to the previous approach, the final solution starts looking like more complicated in terms of design and in terms of product configuration. Let's learn how apply this type of scalability approach (with the highest level of reliability that exists) in our near real-time business monitoring of KPIs scenario.
The situation has now changed. You need to make the solution available in two different data centers, working in a active-active schema and with high availability assured, in case of failure of the primary servers. There is no chance that any event be missed since the operational people from the financial services company must rely with the KPIs to take important decisions.
The two data centers are located in the state of California, but are geographically separated. The first one is based in Redwood, and the other one is based on Palo Alto. These data centers are connected through a fiber optical cable with an 10 GB/s of high bandwidth.
There are some changes that need to take place both in the Oracle CEP application and at the Oracle CEP domain. Let's start with the Oracle CEP application changes. First, you need to modify the EPN assembly descriptor file to include an instance of the following Oracle CEP component: com.oracle.cep.cluster.hagroups.ActiveActiveGroupBean. This special component dynamically manages the group subscriptions available to partition the incoming events between the cluster members.
Secondly, you need to synchronize all the events with all members that belongs to the cluster, including in-flight events. This can be explicitly done using special HA adapters that Oracle CEP make available. Change your EPN flow to include just after of your input adapter an instance of an HA adapter. This adapter need to be aware about which property of your event type carries the information about its age. In that case, you need to configure in this HA adapter the "timeProperty" property. This is necessary to provides guarantees that even in case of failure of one cluster member, the ordering of the events won't be affected. You will also need to create an instance of an HA correlating adapter, just before your output adapter. After this changes, your EPN assembly descriptor should include the following components:
Now here comes the most important change of this approach, which is change the JMS destination from an queue to an topic. This change should be executed both at your JMS provider (because you need to explicitly create an topic endpoint) and at your JMS adapter configuration file. Using JMS topics provides guarantees that all of the JVMs are aware of all events. You will also need to define a events partition criteria at your JMS adapter configuration file. Since topic consumers acts more like subscribers instead of just consumers, every consumer application will receive a copy of all events sent to the topic endpoint. This means that an automatic partition of the events won't happen, but this need to occur in order to provide real scalability.
Thanks to the JMS technology, there is one way of provide some criteria during the messages consumption, which is using selectors. Selectors gives you the possibility to apply the Content Based Router EAI pattern based on existing header/property values. Change the JMS adapter configuration file to include selectors criteria to the message consumption:
Let's understand the changes. The group bindings entries provided tells the JMS adapter which events listen to. The first group binding tells that messages containing "rw" in the site property should be listened only by the Redwood site. The second group binding tells that messages containing "pa" in the site property should be listened only by the Palo Alto site. Each group binding is associated with a group id, which reveals what servers will be able to receive those events. For instance, in the case of the first group binding, only servers associated with the group "ActiveActiveGroupBean_Redwood" will be able to receive events that belongs to the Redwood site. This configuration will ensure that the load of events will be equally distributed across all the JVMs. Let's start the configuration of the Oracle CEP domain.
In the root directory of your Oracle CEP domain, create four copies from one of your current servers, naming them "rw1", "rw2", "pa1" and "pa2" respectively. Since the solution must be available in two different data centers, one in Redwood and another in Palo Alto, these four servers will sustain this topology. The rw1 and rw2 servers will be the Redwood servers, being rw1 the primary and rw2 its backup. The pa1 and pa2 servers will be the Palo Alto servers, being pa1 the primary and pa2 its backup. The idea here is to provide an active-active load balancing between servers rw1 and pa1, each one having its backups on each site. Those backups will ensure high availability for every cluster member. The final topology should be something similar to this:
You need to the change the server's configuration file in order make this topology works. Servers rw1 and rw2 should be part of the "ActiveActiveGroupBean_Redwood" group, and servers pa1 and pa2 should be part of the "ActiveActiveGroupBean_PaloAlto" group. Apply the configuration below on each server's file configuration of the Oracle CEP domain:
Don't forget to change the ports of the "NetIO" and "sslNetIo" components if you plan to execute all those servers in the same server system. In terms of Oracle CEP domain, this is all the configuration necessary, so we are all set in terms of infrastructure. Before starting the tests, we need to adapt the client JMS application in order to provide two things. First, to send the messages to an topic instead to an queue. Second, to provide the "site" property on each message, in order to sustain the message criteria provided in the JMS adapter configuration file. Implement the client JMS application according to the listing below.
This version of the client JMS application didn't change so much compared to its original version using queues, except from the usage of the message property called site. This message property will be used by the selectors as criteria to load-balance the incoming events across all the servers. In the real world, this type of message enrichment is commonly applied by an architectural mechanism that is situated before of the Oracle CEP JVMs, which could be an ESB, an application server or an corporate load balancer. Regardless which mechanism implementation will intend to use, in terms of architecture, it is responsibility of this mechanism to provide this message enrichment, another EAI pattern that must be part of your final solution.
Now we can start the tests. Deploy this new version of the Oracle CEP application into the servers (rw1, rw2, pa1 and pa2) and run again the client JMS application. An recorded result of this third and last test are also available on Youtube. Watch out the video below for how using JMS topics affects the scalability of your Oracle CEP applications.
Today more than ever, solution architects and developers should be aware about what kind of techniques can be applied in their solutions in order to provide real scalability. Trends like Fast Data are biggest motivators for that. This article discussed the importance of scalability, specially when necessary in event-driven solutions. The article, through an didactic business scenario, showed how to apply scalability in Oracle CEP applications, discovering the pros and cons of two different approaches. Finally, the article showed in details how to implement the two approaches in the Oracle CEP example application.
Wednesday Aug 29, 2012
By Ricardo Ferreira-Oracle on Aug 29, 2012
OK, so you are a developer and are starting a new Java EE 6 application using the most wonderful features of the Java EE platform like Enterprise JavaBeans, JavaServer Faces, CDI, JPA e another cool stuff technologies. And your architecture need to hold piece of data into distributed caches to improve application's performance, scalability and reliability?
If this is your current facing scenario, maybe you should look closely in the solutions provided by Oracle WebLogic Server. Oracle had integrated WebLogic Server and its champion data caching technology called Oracle Coherence. This seamless integration between this two products provides a comprehensive environment to develop applications without the complexity of extra Java code to manage cache as a dependency, since Oracle provides an DI ("Dependency Injection") mechanism for Coherence, the same DI mechanism available in standard Java EE applications. This feature is called ActiveCache. In this article, I will show you how to configure ActiveCache in WebLogic and at your Java EE application.
Configuring WebLogic to manage Coherence
Before you start changing your application to use Coherence, you need to configure your Coherence distributed cache. The good news is, you can manage all this stuff without writing a single line of code of XML or even Java. This configuration can be done entirely in the WebLogic administration console. The first thing to do is the setup of a Coherence cluster. A Coherence cluster is a set of Coherence JVMs configured to form one single view of the cache. This means that you can insert or remove members of the cluster without the client application (the application that generates or consume data from the cache) knows about the changes. This concept allows your solution to scale-out without changing the application server JVMs. You can growth your application only in the data grid layer.
To start the configuration, you need to configure an machine that points to the server in which you want to execute the Coherence JVMs. WebLogic Server allows you to do this very easily using the Administration Console. For this example, consider the machine name as "coherence-server".
Remember that in order to the machine concept works, you need to ensure that the NodeManager are being executed in the target server that the machine points to. The NodeManager script can be found in <WLS_HOME>/server/bin/startNodeManager.sh.
The next thing to do is to configure an Coherence cluster. In the WebLogic administration console, navigate to Environment > Coherence Clusters and click in "New" button.
In the field "Name", set the value to "my-coherence-cluster". Click in next.
Specify a valid cluster address and port. The Coherence members will communicate with each other through this address and port. This configuration section tells Coherence to form a cluster using unicast of messages instead of multicast which is the standard. Since the method used will be unicast, you need to configure a valid cluster address and cluster port.
The Coherence cluster has been configured successfully. Now it is time to configure the Coherence members and add them to the cluster. In the WebLogic administration console, navigate to Environment > Coherence Servers and click in "New" button.
In the field "Name" set the value to "coh-server-1". In the field "Machine", associate this new Coherence server to the machine "coherence-server". In the field "Cluster", associate this new Coherence server to the cluster named "my-coherence-cluster". Click in the "Finish" button.
For this example, I will configure only one Coherence server. This means that the Coherence cluster will be composed by only one member. In production scenarios, you will have thousands of Coherence members, all of them distributed in different machines with different configurations. The idea behind Coherence clusters is exactly this: form a virtual data grid composed by different members that will be joined or removed to/from the cluster dynamically.
Before start the Coherence server, you need to configure its classpath. When the JVM of an Coherence server starts, its loads a couple classes that need to be available in runtime. To configure the classpath of the Coherence server, click into the Coherence server name. This action will bring the configuration page of the Coherence server. Click in the "Server Start" tab.
In the "Server Start" tab you will find a lot of fields that can be configured to control the behavior of the Coherence server JVM. In the "Class Path" field, you need to list the following JAR files:
Remember to use a valid file separator compatible with the target operating system that you are using. Click in the "Save" button to update the configuration of the Coherence server.
Now you are ready to go. Start the Coherence server using the "Control" tab of WebLogic administration console. This will instruct WebLogic to send a command to the target machine. This command, once it is received by the target machine, will be responsible to start a new JVM for the Coherence server, according to the parameters that we have configured.
Configuring your Java EE Application to Access Coherence
Now lets pass to the funny part of the configuration. The first thing to do is to inform your Java EE application which Coherence cluster to join. Oracle had updated WebLogic server deployment descriptors so you will not have to change your code or the containers deployment descriptors like application.xml, ejb-jar.xml or web.xml.
In this example, I will show you how to enable DI ("Dependency Injection") to a Coherence cache from a Servlet 3.0 component. In the WEB-INF/weblogic.xml deployment descriptor, put the following metadata information:
As you can see, using the "coherence-cluster-name" tag, we are informing our Java EE application that it should join the "my-coherence-cluster" when it loads in the web container. Without this information, the application will not be able to access the predefined Coherence cluster. It will form its own Coherence cluster without any members. So never forget to put this information.
Now put the coherence.jar and active-cache-1.0.jar dependencies at your WEB-INF/lib application classpath. You need to deploy this dependencies so ActiveCache can automatically take care of the Coherence cluster join phase. This dependencies can be found in the following locations:
Finally, you need to write down the access code to the Coherence cache at your Servlet. In the following example, we have a Servlet 3.0 component that access a Coherence cache named "transactions" and prints into the browser output the content (the ammount property) of one specific transaction.
Thats it! No more configuration is necessary and you have all set to start producing and getting data to/from Coherence. As you can see in the example code, the Coherence cache are treated as a normal dependency in the Java EE container. The magic happens behind the scenes when the ActiveCache allows your application to join the defined Coherence cluster.
The most interesting thing about this approach is, no matter which type of Coherence cache your are using (Distributed, Partitioned, Replicated, WAN-Remote) for the client application, it is just a simple attribute member of com.tangosol.net.NamedCache type. And its all managed by the Java EE container as an dependency. This means that if you inject the same dependency (the Coherence cache named "transactions") in another Java EE component (JSF managed-bean, Stateless EJB) the cache will be the same. Cool isn't it?
Thanks to the CDI technology, we can extend the same support for non-Java EE standards components like simple POJOs. This means that you are not forced to only use Servlets, EJBs or JSF in order to inject Coherence caches. You can do the same approach for regular POJOs created for you and managed by lightweight containers running inside Oracle WebLogic Server.
Sunday Jul 08, 2012
By Ricardo Ferreira-Oracle on Jul 08, 2012
This year I had the pleasure again of being one of the speakers in the TDC ("The Developers Conference") event. I have spoken in this event for three years from now. This year, the main theme of the SOA track was EDA ("Event-Driven Architecture") and I decided to delivery a comprehensive presentation about one of my preferred personal subjects: Real-time using Complex Event Processing. The theme of the presentation was "Business Intelligence in Real-time using CEP & BAM" and I would like to share here the presentation that I have done. The material is in Portuguese since was an Brazilian event that happened in São Paulo.
Once my presentation has a lot of videos, I decided to share the material as a Youtube video, so you can pause, rewind and play again how many times you want it. I strongly recommend you that before starting watching the video, you change the video quality settings to 1080p in High Definition.
Saturday Jun 23, 2012
By Ricardo Ferreira-Oracle on Jun 23, 2012
The concept and usage of data grids are becoming very popular in this days since this type of technology are evolving very fast with some cool lead products like Oracle Coherence. Once for a while, developers need an programmatic way to calculate the total size of a specific cache that are residing in the data grid. In this post, I will show how to accomplish this using Oracle Coherence API. This example has been tested with 3.6, 3.7 and 3.7.1 versions of Oracle Coherence.
To start the development of this example, you need to create a POJO ("Plain Old Java Object") that represents a data structure that will hold user data. This data structure will also create an internal fat so I call that should increase considerably the size of each instance in the heap memory. Create a Java class named "Person" as shown in the listing below.
Now let's create a Java program that will start a data grid into Coherence and will create a cache named "People", that will hold people instances with sequential integer keys. Each person created in this program will trigger the execution of a custom constructor created in the People class that instantiates an internal fat (the random amount of data generated to increase the size of the object) for each person. Create a Java class named "CreatePeopleCacheAndPopulateWithData" as shown in the listing below.
Finally, let's create a Java program that, using the Coherence API and JMX, will calculate the total size of each cache that the data grid is currently managing. The approach used in this example was retrieve every cache that the data grid are currently managing, but if you are interested on an specific cache, the same approach can be used, you should only filter witch cache will be looked for. Create a Java class named "CalculateTheSizeOfPeopleCache" as shown in the listing below.
I've commented the overall example so, I don't think that you should get into trouble to understand it. Basically we are dealing with JMX. The first thing to do is enable JMX support for the Coherence client (ie, an JVM that will only retrieve values from the data grid and will not integrate the cluster) application. This can be done very easily using the runtime "tangosol.coherence.management" system property. Consult the Coherence documentation for JMX to understand the possible values that could be applied. The program creates an in memory data structure that holds a custom class created called "Statistics".
This class represents the information that we are interested to see, which in this case are the size in bytes and in MB of the caches. An instance of this class is created for each cache that are currently managed by the data grid. Using JMX specific methods, we retrieve the information that are relevant for calculate the total size of the caches. To test this example, you should execute first the CreatePeopleCacheAndPopulateWithData.java program and after the CreatePeopleCacheAndPopulateWithData.java program. The results in the console should be something like this:
I hope that this post could save you some time when calculate the total size of Coherence cache became a requirement for your high scalable system using data grids. See you!
Wednesday Apr 18, 2012
By Ricardo Ferreira-Oracle on Apr 18, 2012
In March 28 of 2012, I presented in the Pullman hotel in São Paulo, a whole day workshop about the technical innovations of Oracle WebLogic 12c, plus also the correlated middleware stack that Oracle created around it. This workshop, for those that could be in person, was very informational and productive once without any exception, every presentation was made in practice with the audience.
I would like to share with you the slides I have used in this workshop. The slides content are written in Portuguese, since was a Brazilian workshop for the Brazil folks. Please enjoy it!
Wednesday Feb 22, 2012
By Ricardo Ferreira-Oracle on Feb 22, 2012
When we talk about distributed data grids, elastic caching platforms and in-memory caching technologies, Oracle Coherence is the first option that came in our minds. This happens because Oracle Coherence is the oldest and most mature implementation of data grids, creating successful histories across the world. It is Oracle Coherence the implementation with the bigger number of use cases in the world. Since it's aquisition by Oracle in 2007, the product has been enhanced with powerful enterprise features to remain it's position of the "better of the world" against it's competitors.
This article will help you given your first steps with Oracle Coherence. I have prepared a sequence of three videos that will guide you in the process of creating a data grid cluster, managing data using both Java API and CohQL ("Coherence Query Language") to finally test the reliability and fail over features of the product.
Oracle allows you to download and use any of your products for free, if you are interested in learning or testing the technology. Different of other vendors that put you first in contact with a sales representative or simply not put their software available for download, Oracle encourages you to use the technology so you gain confidence with it. You can download Oracle Coherence at this link. If you don't possess a credential in the OTN ("Oracle Technology Network"), you will be asked to create one.
If you have a powerful computer and a fastest internet bandwidth, change the video quality settings to 1080p HD ("High Definition"). It will improve considerably the quality of your viewing.
Saturday Sep 24, 2011
By Ricardo Ferreira-Oracle on Sep 24, 2011
Welcome to my blog. The purpose of this blog is to provide vital information for you and your team about Oracle Fusion Middleware technologies, turning success a common word in the vocabulary of your projects. I will try to create regular entries at this blog about subjects like tips, architectural guidance, best practices and examples from real world experience with Oracle Fusion Middleware technologies like Tuxedo, Coherence, Application Grid, Web Center, SOA, BPM, EDA and Exalogic.
I little bit about myself. I started my career in 1997 as software engineer, and software development using distributed objects, EAI and enterprise middleware became my passion since. I always been curious about how technologies could be used and combined to create an architectural foundation for software intensive systems and I have got specialized in this subject. After more than a decade working in consulting firms and system integrators as software engineer, developer, architect and team leader, I started to work in software vendors like JBoss, by Red Hat, Progress Software, and currently at Oracle Corporation, definitely one of the most biggest middleware vendors of the world, IMHO, the biggest.
Ricardo Ferreira is just a regular person passionate for technology, traveling, movies and his family. He works for Oracle, member of the Cloud Architects Team, otherwise known as "The A-Team"
- Intelligent Devices with Oracle Edge Analytics & MQTT
- Custom Transports in Oracle Service Bus 12.2.1
- Oracle Service Bus Transport for Apache Kafka (Part 2)
- Oracle Service Bus Transport for Apache Kafka (Part 1)
- Introduction to the Oracle Stream Explorer White Paper
- Getting Started with the REST Adapter in OEP 12c
- Caching in OSB 12c without Out-Of-Process Coherence Servers
- Threading Best Practices for Custom OEP Adapters
- Interoperability between Microsoft and SOA Suite 12c
- Enabling WAN Replication for Oracle Service Bus Result Cache