Recent Posts by Jonathan Purdy

Overview of Scenarios This document describes how applications that are built on Oracle Kubernetes Engine (OKE) can continue operating even if an entire geographic region of Oracle Cloud Infrastructure (OCI) is lost. A basic knowledge of OCI is assumed. Each scenario builds upon the previous ones, describing the incremental design differences that arise in more challenging scenarios. Scenarios start with a simple active-passive stateless application, and then add the...

Introduction Coherence clusters rely on the ability to enlist multiple servers connected by a reasonably reliable network. This allows strong cache consistency guarantees — in the absence of a catastrophic failure, one can reason about state in a Coherence cluster in a similar manner to how one reasons about state in a local memory space. However, a cluster is a poor abstraction for systems that must rely on unreliable networks, which most commonly are systems that span wide...

Because Coherence cache servers are built on top of Java, they must deal with the heap size limitations that arise from garbage collection (GC). This is often at odds with the intended use case for Coherence, which is to manage large amounts of data in memory. In order to fully utilize the available memory on server machines while minimizing the number of JVMs required, we want to use the largest heap sizes that are possible without running into garbage collection problems...

For many developers using Coherence, the first place they look for concurrency control is the com.tangosol.util.ConcurrentMap interface (part of the NamedCache interface). The ConcurrentMap interface includes methods for explicitly locking data. Despite the obvious appeal of a lock-based API, these methods should generally be avoided for a variety of reasons: They are very "chatty" in that they can't be bundled with other operations (such as get and put) and there are no...

While the costs of XA transactions are well known (e.g. increased data contention, higher latency, significant disk I/O for logging, availability challenges, etc.), in many cases they are the most attractive option for coordinating logical transactions across multiple resources. There are a few common approaches when integrating Coherence into applications via the use of an application server's transaction manager: Use of Coherence as a read-only cache, applying transactions...

Disabling hardware multicast (by configuring well-known addresses aka WKA) will place significant stress on the network. For messages that must be sent to multiple servers, rather than having a server send a single packet to the switch and having the switch broadcast that packet to the rest of the cluster, the server must send a packet to each of the other servers. While hardware varies significantly, consider that a server with a single gigabit connection can send at most...

Applications that use write-behind caching and wish to query the logical entity set have the option of querying the NamedCache itself or querying the database. In the former case, no particular restrictions exist beyond the limitations intrinsic to the Coherence query engine itself. In the latter case, queries may see partially committed transactions (e.g. with a parent-child relationship, the version of the parent may be different than the version of the child objects) and/or...

Applications that rely on partial caches of databases, and use read-through to maintain those caches, have some trade-offs if queries are required. Coherence does not support push-down queries, so queries will apply only to data that currently exists in the cache. This is technically consistent with "read committed" semantics, but the potential absence of data may make the results so unintuitive as to be useless for most use cases (depending on how much of the database is...

Some NamedCache methods (including clear(), entrySet(Filter), aggregate(Filter, …), invoke(Filter, …)) may generate large intermediate results. The size of these intermediate results may result in out-of-memory exceptions on cache servers, and in some cases on cache clients. This may be particularly problematic if out-of-memory exceptions occur on more than one server (since these operations may be cluster-wide) or if these exceptions cause additional memory use on the...