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Keeping all instance of in memory graph db in sync

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.

Synchronise a variable between java instance across network

I have this assignment in college where they ask us to run a Java app as a socket server with multiple clients. Client sends a string, server returns the string in upper case with a request counter. Quite simple.
Each request made by any given client is counted on the server side and stored in a static variable for each client connection thread. So that each client request increments the counter globally on the server. That's working well.
Now, they ask us to run "backup" instances of that server on different machines on the network so that if the primary stops responding, the client connects to one of the backups. That, I got working. But the counter is obviously reset since it's a different server.
The challenge is that the request counter be the same on the primary and the secondaries so that if the primary responds to 10 requests, goes down, client switch to a backup and makes a request, the backup server responds 11.
Here is what I considered:
if on the same PC, I'd use threads but we're over the network so I
believe this will not work.
server sends that counter to the client
with the response, which in turn returns it to the server at the
next request and so forth. Not very "clean" imo but could work.
Each server talks to each other to sync this counter. However, sockets
don't seem to be very efficient to do this, if even possible. RMI
seems to be an alternative here but I'd like confirmation before I
start learning it.
Any leads or suggestions here? I'm not posting code because I don't need a solution here but if necessary, I can invite to the gihub repo.
EDIT: There is no latency, reliability or similar constraints for this project. There is X number of clients and Y number of servers (single master, multiple failovers). Additional third party infrastructure like a DB isn't an option really but third party Java librairies are welcome. Basically I just run in Eclipse on multiple PCs. This is an introduction assignment to distributed systems, expected done in 2 weeks so I believe "keep it simple" is the key here!
EDIT 2: The number and addresses of backup servers will be passed as arguments to the application so broadcast/discovery isn't necessary. We'll likely cover all those points in a later lab assignment in the semester :)
EDIT 3: From all your great suggestions, I'll try an implementation of some variation of #3 and let you know how it works. I think the issue I have here is to make sure all servers are aware of the others. But like I mentioned, they don't need to discover each other so I'll hard code it for now and revisit in the next assignment! Probably opt for some elected master... :)

If option #2 is allowed, then it is the easiest, however I am not sure how it could work in the face of multiple clients (so it depends on the requirements here).
Is it possible to back the servers by a shared db running on another computer? Ideally perhaps one clustered across multiple machines? Or can you use an event bus or 3rd party libraries / code (shared cache, JMS, or even EJBs)?
If not, then having the servers talk to each other is your best bet. Sockets can work, as could UDP multicast (careful there though, no way to know if a message was missed which is why TCP / sockets are safer). If the nodes are going to talk to each other there are generally a few accepted ways to handle the setup:
Master / slaves: Current node is the master and all writes are to it. Slaves connect to the master and receive updates. When the master goes down a new master needs to be elected (see leader election). MongoDB works like this.
Everyone to everyone: Every node connects to every other known node. Can get complicated and might not scale well to lots of nodes.
Daisy chain: one node connects to the next node, which connects to the next, and so on. I don't believe this is widely used.
Ring network: Each node connects to two others in order to form a ring. This is generally superior to daisy chain, but a little bit more complicated to implement.
See here for more examples: https://en.wikipedia.org/wiki/Network_topology
If this was in the real world (i.e. not school), you would use either a shared cache (e.g. ehcache), local caches backed by an event bus (JMS of some sort), or a shared clustered db.
EDIT:
After re-reading your question, it seems you only have a single backup server to worry about, and my guess of the course requirements is that they simply want your backup server to connect to your primary server and also receive the variable count updates. This is completely fine to implement with sockets (it isn't inefficient for a single backup server), and is perhaps the solution they are expecting you to use.
E.g. Backup server connects to primary server and either polls for updates across the held connection or simply listens for updates issued from the primary server.
Key notes:
- You might need keep alives to ensure the connection does not get killed.
- Make sure to implement re-connection logic if the connection from backup to primary dies.
If this is for a networking course they may be expecting UDP multicast, but that may depend a little bit on the server / network environment.

This is a classic distributed systems problem. The right solution is some variation of your option #3, where the different servers communicate with each other.
Where it gets complicated is when you start to introduce latency, downtime, and/or network partitioning between the various servers. Eventually you'll need to arrive at some kind of consensus algorithm. Paxos is a well-known approach to this problem, but there are others; Raft is popular these days as well.

In my opinion best solution is to have vector of the counters. One counter per one server. Each server increments its own counter and broadcast vector value to all other servers. This data structure is conflict-free replicated data type.
Number of requests is calculated as sum of all elements of the vector.
About consistency. If you need strictly growing number on all servers you need to synchronously replicate you new value before answer to client.
The penalty here is performance and availability.
About broadcasting. You can choose any broadcasting algorithm you want. If number of servers are not too large you can use full mesh topology. If number of server become large you can use ring or star topology to replicate data.

The most real life would be option 3. It happens all the time. Nodes talk to one another on another port. So they self discover by broadcast (UDP). So each server broad casts its max on a UDP port. Other nodes listen and up their value that + 1 if their current value is less than that value, else ignore it and instead broadcast their bigger value.
This will work best when there is a 2-300 gap between client calls. This also assumes that any server could be primary (as decided by a load balancer).
UDP is stable within a LAN. Used widely.

Solutions to this problem trade off speed against consistency.
If you value consistency over speed you could try a synchronous approach (assuming servers A, B and C):
A receives initial request
A opens connection to B and C to request current counts from each
A calculates max count (based on its own value and the values from B and C), adds one and sends new count to B and C
A closes connections to B and C
A replies to original request, including new max count
At this point, all servers are in sync with the new max count, ready for a new request to any server.
Edit: Of course, if you are able to use a shared database, the problem becomes much simpler.

Module clustering and JMS

I have a module which runs standalone in a JVM (no containers) and communicates with other modules via JMS.
My module is both a producer in one queue and a consumer in a different queue.
I have then need to cluster this module, both for HA reasons and for workload reasons, and I'm probably going to go with Terracotta+Hibernate for clustering my entities.
Currently when my app starts it launches a thread (via Executors.newSingleThreadExecutor()) which serves as the consumer (I can attach actual code sample if relevant and neccessary).
What I understood from reading questions here is that if I just start up my module on N different JVMs then N different subscribers will be created and each message in the queue will arrive to N subscribers.
What I'd like to do is have only one of them (let's currently say that which one is not important) process that message and so in actuality enable me to process N messages at a time.
How can/should this be done? Am I way off the track?
BTW, I'm using OpenMQ as my implementation but I don't know if that's relevant.
Thanks for any help

A classic case of message handling in clustered environment. This is what I would do.
Use Broadcast message (Channel based) in place of Queue. Queue being useful for point to point communication is not very effective. Set validity of message till the time it is consumed by one of the consumer. This way, other consumers wont even see the message and only one consumer will consume it.

Take a look at JGroups. You may consider implementing your module/subscribers to use jgroups for the kind of synchronization you need. JGroups provide Reliable Multicast Communication.

Potential pitfalls in using a JMS queue?

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.

Critically efficient server

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.