Continuous Query


While it is possible to obtain a point in time query result from a Coherence cache, and it is possible to receive events that would change the result of that query, Coherence provides a feature that combines a query result with a continuous stream of related events in order to maintain an up-to-date query result in a real-time fashion. This capability is called Continuous Query, because it has the same effect as if the desired query had zero latency and the query were being executed several times every millisecond!

Coherence implements the Continuous Query functionality by materializing the results of the query into a Continuous Query Cache, and then keeping that cache up-to-date in real-time using event listeners on the query. In other words, a Coherence Continuous Query is a cached query result that never gets out-of-date.

Uses of Continuous Query Caching

There are several different general use categories for Continuous Query Caching:

An example use case is a trading system desktop, in which a trader's open orders and all related information needs to be maintained in an up-to-date manner at all times. By combining the Coherence*Extend functionality with Continuous Query Caching, an application can support literally tens of thousands of concurrent users.

Continuous Query Caches are useful in almost every type of application, including both client-based and server-based applications, because they provide the ability to very easily and efficiently maintain an up-to-date local copy of a specified sub-set of a much larger and potentially distributed cached data set.

The Coherence Continuous Query Cache

The Coherence implementation of Continuous Query is found in the class. This class, like all Coherence caches, implements the standard NamedCache interface, which includes the following capabilities:

Since the ContinuousQueryCache implements the NamedCache interface, which is the same API provided by all Coherence caches, it is extremely simple to use, and it can be easily substituted for another cache when its functionality is called for.

Constructing a Continuous Query Cache

There are two items that define a Continuous Query Cache:

  1. The underlying cache that it is based on;
  2. A query of that underlying cache that produces the sub-set that the Continuous Query Cache will cache.

The underlying cache is any Coherence cache, including another Continuous Query Cache. A cache is usually obtained from a CacheFactory, which allows the developer to simply specify the name of the cache and have it automatically configured based on the application's cache configuration information; for example:

NamedCache cache = CacheFactory.getCache("orders");

The query is the same type of query that would be used to query any other cache; for example:

Filter filter = new AndFilter(new EqualsFilter("getTrader", traderid),
                              new EqualsFilter("getStatus", Status.OPEN));

Normally, to query a cache, one of the methods from the QueryMap is used; for examples, to obtain a snap-shot of all open trades for this trader:

Set setOpenTrades = cache.entrySet(filter);

Similarly, the Continuous Query Cache is constructed from those same two pieces:

ContinuousQueryCache cacheOpenTrades = new ContinuousQueryCache(cache, filter);
Cleaning up the resources associated with a ContinuousQueryCache

A ContinuousQueryCache places one or more event listeners on its underlying cache. If the ContinuousQueryCache is used for the duration of the application, then the resources will be cleaned up when the node is shut down or otherwise stops. However, if the ContinuousQueryCache is only used for a period of time, then when the application is done using it, the application must call the release() method on the ContinuousQueryCache.

Caching only keys, or caching both keys and values

When constructing a ContinuousQueryCache, it is possible to specify that the cache should only keep track of the keys that result from the query, and obtain the values from the underlying cache only when they are asked for. This feature may be useful for creating a ContinuousQueryCache that represents a very large query result set, or if the values are never or rarely requested. To specify that only the keys should be cached, use the constructor that allows the CacheValues property to be configured; for example:

ContinuousQueryCache cacheOpenTrades = new ContinuousQueryCache(cache, filter, false);

If necessary, the CacheValues property can also be modified after the cache has been instantiated; for example:

CacheValues property and Event Listeners

If the ContinuousQueryCache has any standard (non-lite) event listeners, or if any of the event listeners are filtered, then the CacheValues property will automatically be set to true, because the ContinuousQueryCache uses the locally cached values to filter events and to supply the old and new values for the events that it raises.

Listening to the ContinuousQueryCache

Since the ContinuousQueryCache is itself observable, it is possible for the client to place one or more event listeners onto it. For example:

ContinuousQueryCache cacheOpenTrades = new ContinuousQueryCache(cache, filter);

Assuming some processing has to occur against every item that is already in the cache and every item added to the cache, there are two approaches. First, the processing could occur then a listener could be added to handle any later additions:

ContinuousQueryCache cacheOpenTrades = new ContinuousQueryCache(cache, filter);
for (Iterator iter = cacheOpenTrades.entrySet().iterator(); iter.hasNext(); )
    Map.Entry entry = (Map.Entry);
    // .. process the cache entry

However, that code is incorrect because it allows events that occur in the split second after the iteration and before the listener is added to be missed! The alternative is to add a listener first, so no events are missed, and then do the processing:

ContinuousQueryCache cacheOpenTrades = new ContinuousQueryCache(cache, filter);
for (Iterator iter = cacheOpenTrades.entrySet().iterator(); iter.hasNext(); )
    Map.Entry entry = (Map.Entry);
    // .. process the cache entry

However, it is possible that the same entry will show up in both an event an in the Iterator, and the events can be asynchronous, so the sequence of operations cannot be guaranteed.

The solution is to provide the listener during construction, and it will receive one event for each item that is in the Continuous Query Cache, whether it was there to begin with (because it was in the query) or if it got added during or after the construction of the cache:

ContinuousQueryCache cacheOpenTrades = new ContinuousQueryCache(cache, filter, listener);
Achieving a stable materialized view

The ContinuousQueryCache implementation faced the same challenge: How to assemble an exact point-in-time snapshot of an underlying cache while receiving a stream of modification events from that same cache. The solution has several parts. First, Coherence supports an option for synchronous events, which provides a set of ordering guarantees. Secondly, the ContinuousQueryCache has a two-phase implementation of its initial population that allows it to first query the underlying cache and then subsequently resolve all of the events that came in during the first phase. Since achieving these guarantees of data visibility without any missing or repeated events is fairly complex, the ContinuousQueryCache allows a developer to pass a listener during construction, thus avoiding exposing these same complexities to the application developer.

Support for synchronous and asynchronous listeners

By default, listeners to the ContinuousQueryCache will have their events delivered asynchronously. However, the ContinuousQueryCache does respect the option for synchronous events as provided by the SynchronousListener interface.

Making the ContinuousQueryCache Read-Only

The ContinuousQueryCache can be made into a read-only cache; for example:


A read-only ContinuousQueryCache will not allow objects to be added to, changed in, removed from or locked in the cache.

Once a ContinuousQueryCache has been set to read-only, it cannot be changed back to read/write.