We are building an java application which will use embedded Neo4j for graph traversal. Below are the reasons why we want to use embedded version instead of centralized server
This app is not a data owner. Data will be ingested on it through other app. Keeping data locally will help us in doing quick calculation and hence it will improve our api sla.
Since data foot print is small we don't want to maintain centralized server which will incur additional cost and maintenance.
No need for additional cache
Now this architecture bring two challenges. First How to update data in all instance of embedded Neo4j application at same time. Second how to make sure that all instance are in sync i.e using same version of data.
We thought of using Kafka to solve first problem. Idea is to have kafka listener with different groupid(to ensure all get updates) in all instance . Whenever there is update, event will be posted in kafka. All instance will listen for event and will perform the update operation.
However we still don't have any solid design to solve second problem. For various reason one of the instance can miss the event (it's consumer is down). One of the way is to keep checking latest version by calling api of data owner app. If version is behind replay the events.But this brings additional complexity of maintaining the event logs of all updates. Do you guys think if it can be done in a better and simpler way?
Kafka consumers are extremely consistent and reliable once you have them configured properly, so there shouldn't be any reason for them to miss messages, unless there's an infrastructure problem, in which case any solution you architect will have problems. If the Kafka cluster is healthy (e.g. at least one of the copies of the data is available, and at least quorum zookeepers are up and running), then your consumers should receive every single message from the topics they're subscribed to. The consumer will handle the retries/reconnecting itself, as long as your timeout/retry configurations are sane. The default configs in the latest kafka versions are adequate 99% of the time.
Separately, you can add a separate thread, for example, that is constantly checking what the latest offset is per topic/partitions, and compare it to what the consumer has last received, and maybe issue an alert/warning if there is a discrepancy. In my experience, and with Kafka's reliability, it should be unnecessary, but it can give you peace of mind, and shouldn't be too difficult to add.
Related
I can use the Java MQ Api to put and get messages.
I can also disable gets and put on a queue.
During a migration project, we'll have an App running in parallell. Old and New. Old and New will have their own separate queues. I regulary have messages from a client going to Old. Occasionally want the msgs to flow to New instead.
wondering if MQ supports a gate/switch concept. where via API I can point a queue to go only to New, or only to Old, for a short time.
Trying to avoid going to message based routing via WMB since I dont have to do that today. THe parallel mode is only for a few months.
You do not mention the version of MQ or whether there are message affinities or dependence on preserving the MQMD.MsgID. These are critical in devising a solution to this problem. I'll try to describe enough options so that at least one will be viable whatever version you are at.
Pub/Sub
The easiest thing to do is to have the messages arrive on an alias over a topic. Any message that arrives is published immediately on that topic. Then it is a simple matter to generate administrative subscriptions to direct messages to the queues on which the apps needing the messages are listening. This is entirely a configuration change and requires no external components, processes or code. It is available from v7.1 of MQ and higher, which is to say any of the currently supported versions of MQ.
The down side is that IBM MQ will change the MQMD.MsgID from the time the message is received on the topic to the time it is published on the application's input queue. This breaks the app's ability to use the MQMD.MsgID of the incoming message as a correlation ID when replying. If the requesting app pre-loads the correlation ID or doesn't rely on a correlation ID, this is not an issue.
Aliasing
But for apps where this is an issue, it gets a bit harder. You can alias over a queue and have inbound messages land on the alias. When you need to switch from one queue to another, you change the alias. There are a couple issues with this. The first is that it is never possible to deliver the message stream to more than one of the applications. In a parallel processing test it is often desirable to do exactly that and then compare summary or detail reports.
The second problem is more operational in nature. It isn't possible to change the alias while it is open. If the messages arrive over a RCVR, RQSTR or `CLUSRCVR channel, no problem. Stop the channe, switch the alias and restart the channel. In a series of MQSC script commands this can be done faster than it can be typed. However, if the applications putting the messages are connected in bindings mode or via client directly to the alias, they must all be stopped in order to change the alias.
That said, aliasing works on all versions of MQ out of the box.
Physical copy
One solution that's been around for quite some time is to use the Q program (SupportPac MA01) to direct the messages. In this scenario, the queue on which messages land is a local queue. The Q program is either triggered or set to constantly listen on the queue. When a message arrives, Q then copies it to one or both of the destination queues.
Switching the behavior if Q is triggered involves pre-defining 2 or 3 processes where each defines a different behavior - move new messages to QUEUEA, to QUEUEB or to both. Changing the queue's PROCESS attribute to point to a different process results in an instantaneous change of the behavior.
Alternatively, if Q is configured to listen on the queue forever then changing the behavior involves use of three different scripts to execute it where one causes messages to be copied to QUEUEA, another to QUEUEB and another to both queues. Changing the behavior involves killing the script and starting a different one.
The Q program works with all versions of MQ, regardless of whether it is triggered or scripted.
Downsides to this approach include the obvious - more moving parts. You have to trigger the queue or else make a transactional program act like a daemon. Not hard but if you are betting the business on it then perhaps some monitoring is in order to make sure the input queue doesn't start building.
Recommendation
Of all these methods, I really like the Pub/Sub version. It is extremely reliable, has the least moving parts, and if anything breaks it's under IBM support. When you need to change something, you can do that with minimal impact to the running applications. If at all possible, use that.
I've got a Spring Web application that's running on two different instances.
The two instances aren't aware of each other, they run on distinct servers.
That application has a scheduled Quartz job but my problem is that the job shouldn't execute simultaneously on the instances, as its a mail sending job, it could cause duplicate emails being sent.
I'm using RAMJobStore and JDBCJobStore is not an option for me due to the large number of tables it requires.(I cant afford to create many tables due to internal restriction)
The solutions I thought about:
-creating a single control table, that has to be checked everytime a job starts (with repeatable read isolation level to avoid concurrency issues) The problem is that if the server is killed, the table might be left in a invalid state.
-using properties to define a single server to be the job running server. Problem is that if that server goes down, jobs will stop running
Has anyone ever experienced this problem and do you have any thoughts to share?
Start with the second solution (deactivate qartz on all nodes except one). It is very simple to do and it is safe. Count how frequently your server goes down. If it is inacceptable then try the first solution. The problem with the first solution is that you need a good skill in mutithreaded programming to implement it without bugs. It is not so simple if multithreading is not your everyday task. And a cost of some bug in your implementation may be bigger than actual profit.
I've been asked to design and implement a system for receiving a high volume of automated sensor data from a large number of devices. This data will be produced at regular intervals and sent to the server as xml in an http post. The devices will keep resending the same data if they don't receive a specific acknowledgment from the server. Some potentially heavy duty processing of this data will need to occur before it's inserted to a number of tables in the main database via a transaction, and additionally some data points will need to be enqueued to be re-directed to other external urls.
I'm planning on using a Java application server (leaning towards GlassFish) with a servlet to receive the incoming data. I'd like to implement some kind of queuing mechanism to store the data temporarily so that the response back to the sensor isn't dependent on all the intermediate processing. Separate independent queues are also a requirement for the data re-direction piece. After doing some research the two main options seem to be:
1) Install a database on the app server and use tables for the various queues. The queues would be processed by a Java application, either running in the app server or standalone as it's own service.
2) Use a database backed JMS solution to implement the queuing.
I'm not that familiar with JMS but from what I've read it seems to be the better solution in this case. The primary requirement is that no sensor data ever be lost or dropped from the queue before being processed and that it be processed more or less sequentially. We'd also like to make it easy to halt the processing of some of the queues at certain times but still have them accumulate data and for these messages to never automatically expire.
With strategy 1 it's obvious to me how to meet these requirements but it may be less robust and scalable, and more complex to develop than strategy 2, since I'll need to write my own multi-threaded code to handle the various independent queues. I'm wondering what the potential pitfalls could be in using JMS queues for this purpose since I've never worked with them before.
Data integrity is a big issue so I need to make sure JMS can guarantee no data loss in the event of a server reboot, power outage, or if the queue gets very large for some reason. For instance could a problem completing transactions to the main database for a period of time potentially cause the JVM to run out of memory, crash, and lose all accumulated data? (This would be the nightmare scenario).
Also, I was wondering if there would be any way to pause the JMS queue processing via an app server admin tool or to easily see what's in the queue (I would be enqueuing an object which would be the message xml plus some other data, including timestamp received, etc.) I've read a few posts on here that deal with related issues but wanted to get some direct feedback. Basically I'd like to know of instances (if any) where JMS is not an appropriate queuing solution and if this is one of those cases. Any advice is greatly appreciated.
Kaleb's answer talks about the benefits of JMS quite eloquently, but since you're asking about pitfalls, here's what I can think of.
Not all JMS implementations are equal. In theory you can use whatever implementation suits your needs, but unless you're prepared to do some serious load testing and failure condition testing, you can't know that a particular implementation isn't going to fail under your particular use case.
Most JMS use a transactional datastore like a relational database as their back end. That means that rather than writing directly to whatever datastore you're familiar with, you have to rely on the JMS implementation's extra layer between you and that stored messages.
While swapping JMS implementations to find the one that perfectly fits your needs may seem like a simple endeavor because of the homogeneous JMS API, the critical features for failure handling, JMS server monitoring, and all the other cool stuff that exists above and beyond messaging is going to be a hassle to deal with if you do change your implementation.
That said, I think you'd be crazy to write to the DB yourself instead of going with JMS. On the first point, ActiveMQ is a venerable JMS server used in many enterprise environments. On the second point, the fact is you'd just end up writing that extra layer yourself in order to implement messaging, and your code won't have the benefit of thousands of eyes (or a set of paid developers who's sole job it is to respond to customers and make sure the JMS implementation is solid). On the third point, well the same ends up being true of your backend datastore. Use JMS, you'll save yourself trouble in the long run.
If you want to go the JMS route, a standalone JMS-compatible message broker (separate from your app server) would be a good choice. Message brokers range from free open-source (like ActiveMQ at http://activemq.apache.org/ or OpenMQ at https://mq.dev.java.net/), to large-scale commercial solutions (IBM's WebSphere MQ at http://www-01.ibm.com/software/integration/wmq/ is one of the largest).
Message brokers offer guaranteed delivery (provided the server's up and listening), and you can do quite a bit to ensure that the system is fail-safe including integrated backup broker servers and instant power backup. Broker queues can eventually run out of room if your app server isn't picking up the messages, but you can assign huge queue depth (100's of GB) and have the server send alerts if the messages aren't getting processed and the queue reaches a certain percentage.
Your Java app would then run on a different server entirely, and would connect to the broker and pull messages off of the queue as fast as possible. If the app server crashes or stops picking up messages for any other reason, the broker would just keep all messages in that queue until the app server begins picking them up again.
You will be wanting to implement a poison message queue in your implementation - this is the place that messages unable to be processed after some number of retries will arrive.
You will probably need to write some code that can examine the messages in that queue and re-send them to the appropriate destination after fixing whatever is causing them to fail.
If sequence of message processing is important, a message ending up in the poison queue could mean all processing is halted until that message is corrected.
As far as fault tolerance goes, you can have multiple instances of the consuming services subscribe to the same queue or topic, providing an ability to continue processing even if one or more instances goes down.
Finally, have a watchdog process that pings the various consumers on your message queue, and if one doesn't respond, have it send a message that results in a new instance being started. In this way, your message processing environment can be somewhat self regulating.
I have a situation here where I need to distribute work over to multiple JAVA processes running in different JVMs, probably different machines.
Lets say I have a table with records 1 to 1000. I am looking for work to be collected and distributed is sets of 10. Lets say records 1-10 to workerOne. Then records 11-20 to workerThree. And so on and so forth. Needless to say workerOne never does the work of workerTwo unless and until workerTwo couldnt do it.
This example was purely based on database but could be extended to any system, I believe be it File processing, email processing and so forth.
I have a small feeling that the immediate response would be to go for a Master/Worker approach. However here we are talking about different JVMs. Even if one JVM were to come down the other JVM should just keep doing its work.
Now the million dollar question would be: Are there any good frameworks(production ready) that would give me facility to do this. Even if there are concrete implementations of specific needs like Database records, File processing, Email processing and their likes.
I have seen the Java Parallel Execution Framework, but am not sure if it can be used for different JVMs and if one were to come down would the other keep going.I believe Workers could be on multiple JVMs, but what about the Master?
More Info 1: Hadoop would be a problem because of the JDK 1.6 requirement. Thats bit too much.
Thanks,
Franklin
Might want to look into MapReduce and Hadoop
You could also use message queues. Have one process that generates the list of work and packages it in nice little chunks. It then plops those chunks on a queue. Each one of the workers just keeps waiting on the queue for something to show up. When it does, the worker pulls a chunk off the queue and processes it. If one process goes down, some other process will pick up the slack. Simple and people have been doing it that way for a long time so there's a lot information about it on the net.
Check out Hadoop
I believe Terracotta can do this. If you are dealing with web pages, JBoss can be clustered.
If you want to do this yourself you will need a work manager which keeps track of jobs to do, jobs in progress and jobs never done which needs to be rescheduled. The workers then ask for something to do, do it, and send the result back, asking for more.
You may want to elaborate on what kind of work you want to do.
The problem you've described is definitely best solved using the master/worker pattern.
You should have a look into JavaSpaces (part of the Jini framework), it's really well suited to this kind of thing. Basically you just want to encapsulate each task to be carried out inside a Command object, subclassing as necesssary. Dump these into the JavaSpace, let your workers grab and process one at a time, then reassemble when done.
Of course your performance gains will totally depend on how long it takes you to process each set of records, but JavaSpaces won't cause any problems if distributed across several machines.
If you work on records in a single database, consider performing the work within the database itself using stored procedures. The gain for processing the records on different machine might be negated by the cost of retrieving and transmitting the work between the database and the computing nodes.
For file processing it could be a similar case. Working on files in (shared) filesystem might introduce large I/O pressure for OS.
And the cost for maintaining multiple JVM's on multiple machines might be an overkill too.
And for the question: I used the JADE (Java Agent Development Environment) for some distributed simulation once. Its multi-machine suppord and message passing nature might help you.
I would consider using Jgroups for that. You can cluster your jvms and one of your nodes can be selected as master and then can distribute the work to the other nodes by sending message over network. Or you can already partition your work items and then manage in master node the distribution of the partitions like partion-1 one goes to JVM-4 , partion-2 goes to JVM-3, partion-3 goes to JVM-2 and so on. And if JVM-4 goes down it will be realized by the master node and then master node will tell to one of the other nodes to start pick up partition-1 as well.
One other alternative which is easier to use is redis pub sub support. http://redis.io/topics/pubsub . But then you will have to maintain redis servers which i dont like.
I am developing a client-server based application for financial alerts, where the client can set a value as the alert for a chosen financial instrument , and when this value will be reached the monitoring server will somehow alert the client (email, sms ... not important) .The server will monitor updates that come from a data generator program. Now, the server has to be very efficient as it has to handle many clients (possible over 50-100.000 alerts ,with updates coming at 1,2 seconds) .I've written servers before , but never with such imposed performances and I'm simply afraid that a basic approach(like before) will just not do it . So how should I design the server ?, what kind of data structures are best suited ?..what about multithreading ?....in general what should I do (and what I should not do) to squeeze every drop of performance out of it ?
Thanks.
I've worked on servers like this before. They were all written in C (or fairly simple C++). But they were even higher performance -- handling 20K updates per second (all updates from most major stock exchanges).
We would focus on not copying memory around. We were very careful in what STL classes we used. As far as updates, each financial instrument would be an object, and any clients that wanted to hear about that instrument would subscribe to it (ie get added to a list).
The server was multi-threaded, but not heavily so -- maybe a thread handing incoming updates, one handling outgoing client updates, one handling client subscribe/release notifications (don't remember that part -- just remember it had fewer threads than I would have expected, but not just one).
EDIT: Oh, and before I forget, the number of financial transactions happening is growing at an exponential rate. That 20K/sec server was just barely keeping up and the architects were getting stressed about what to do next year. I hear all major financial firms are facing similar problems.
You might want to look into using a proven message queue system, as it sounds like this is basically what you are doing in your application.
Projects like Apache's ActiveMQ or RabbitMQ are already widely used and highly tuned, and should be able to support the type of load you are talking about outside of the box.
I would think that squeezing every drop of performance out of it is not what you want to do, as you really never want that server to be under load significant enough to take it out of a real-time response scenario.
Instead, I would use a separate machine to handle messaging clients, and let that main, critical server focus directly on processing input data in "real time" to watch for alert criteria.
Best advice is to design your server so that it scales horizontally.
This means distributing your input events to one or more servers (on the same or different machines), that individually decide whether they need to handle a particular message.
Will you be supporting 50,000 clients on day 1? Then that should be your focus: how easily can you define a single client's needs, and how many clients can you support on a single server?
Second-best advice is not to artificially constrain yourself. If you say "we can't afford to have more than one machine," then you've already set yourself up for failure.
Beware of any architecture that needs clustered application servers to get a reasonable degree of performance. London Stock Exchange had just such a problem recently when they pulled an existing Tandem-based system and replaced it with clustered .Net servers.
You will have a lot of trouble getting this type of performance from a single Java or .Net server - really you need to consider C or C++. A clustered architecture is much more error prone to build and deploy and harder to guarantee uptime from.
For really high volumes you need to think in terms of using asynchronous I/O for networking (i.e. poll(), select() and asynchronous writes or their Windows equivalents), possibly with a pool of worker threads. Read up about the C10K problem for some more insight into this.
There is a very mature C++ framework called ACE (Adaptive Communications Environment) which was designed for high volume server applications in telecommunications. It may be a good foundation for your product - it has support for quite a variety of concurrency models and deals with most of the nuts and bolts of synchronisation within the framework. You might find that the time spent learning how to drive this framework pays you back in less development and easier implementation and testing.
One Thread for the receiving of instrument updates which will process the update and put it in a BlockingQueue.
One Thread to take the update from the BlockingQueue and hand it off to the process that handles that instrument, or set of instruments. This process will need to serialize the events to an instrument so the customer will not receive notices out-of-order.
This process (Thread) will need to iterated through the list of customers registered to receive notification and create a list of customers who should be notified based on their criteria. The process should then hand off the list to another process that will notify the customer of the change.
The notification process should iterate through the list and send each notification event to another process that handles how the customer wants to be notified (email, etc.).
One of the problems will be that with 100,000 customers synchronizing access to the list of customers and their criteria to be monitored.
You should try to find a way to organize the alerts as a tree and be able to quickly decide what alerts can be triggered by an update.
For example let's assume that the alert is the level of a certain indicator. Said indicator can have a range of 0, n. I would groups the clients who want to be notified of the level of the said indicator in a sort of a binary tree. That way you can scale it properly (you can actually implement a subtree as a process on a different machine) and the number of matches required to find the proper subset of clients will always be logarithmic.
Probably the Apache Mina network application framework as well as Apache Camel for messages routing are the good start point. Also Kilim message-passing framework looks very promising.