This chapter provides a checklist of areas that should be planned for and considered when moving from a development or test environment to a production environment. Solutions and best practices are provided an should be implemented as required. Additional recommendation when using Coherence*Extend can be found in Oracle Coherence Client Guide.
The following sections are included in this chapter:
A unique cluster ensures that cluster members do not accidentally join existing clusters that are on the network. To ensure that the clusters are completely isolated, configure a dedicated multicast IP address and port for each cluster and configure a unique cluster name. See Oracle Coherence Developer's Guide for details.
After the POC or prototype stage is complete, and until load testing begins, it is not out of the ordinary for an application to be developed and tested by engineers in a non-clustered form. Testing primarily in the non-clustered configuration can hide problems with the application architecture and implementation that appear later in staging or even production.
Make sure that the application has been tested in a clustered configuration before moving to production. There are several ways for clustered testing to be a natural part of the development process; for example:
Developers can test with a locally clustered configuration (at least two instances running on their own computer). This works well with the
TTL=0 setting, since clustering on a single computer works with the
Unit and regression tests can be introduced that run in a test environment that is clustered. This may help automate certain types of clustered testing that an individual developer would not always remember (or have the time) to do.
Most production networks are based on 10 Gigabit Ethernet (10GbE), with some still built on Gigabit Ethernet (GbE) and 100Mb Ethernet. For Coherence, GbE and 10GbE are suggested and 10GbE is recommended. Most servers support 10GbE, and switches are economical, highly available, and widely deployed.
It is important to understand the topology of the production network, and what the devices are used to connect all of the servers that run Coherence. For example, if there are ten different switches being used to connect the servers, are they all the same type (make and model) of switch? Are they all the same speed? Do the servers support the network speeds that are available?
In general, all servers should share a reliable, fully switched network. This generally implies sharing a single switch (ideally, two parallel switches and two network cards per server for availability). There are two primary reasons for this. The first is that using multiple switches almost always results in a reduction in effective network capacity. The second is that multi-switch environments are more likely to have network partitioning events where a partial network failure results in two or more disconnected sets of servers. While partitioning events are rare, Coherence cache servers ideally should share a common switch.
To demonstrate the impact of multiple switches on bandwidth, consider several servers plugged into a single switch. As additional servers are added, each server receives dedicated bandwidth from the switch backplane. For example, on a fully switched gigabit backplane, each server receives a gigabit of inbound bandwidth and a gigabit of outbound bandwidth for a total of 2Gbps full duplex bandwidth. Four servers would have an aggregate of 8Gbps bandwidth. Eight servers would have an aggregate of 16Gbps. And so on up to the limit of the switch (in practice, usually in the range of 160-192Gbps for a gigabit switch). However, consider the case of two switches connected by a 4Gbps (8Gbps full duplex) link. In this case, as servers are added to each switch, they have full mesh bandwidth up to a limit of four servers on each switch (that is, all four servers on one switch can communicate at full speed with the four servers on the other switch). However, adding additional servers potentially create a bottleneck on the inter-switch link. For example, if five servers on one switch send data to five servers on the other switch at 1Gbps per server, then the combined 5Gbps is restricted by the 4Gbps link. Note that the actual limit may be much higher depending on the traffic-per-server and also the portion of traffic that must actually move across the link. Also note that other factors such as network protocol overhead and uneven traffic patterns may make the usable limit much lower from an application perspective.
Avoid mixing and matching network speeds: Make sure that all servers connect to the network at the same speed and that all of the switches and routers between those servers run at that same speed or faster.
Before deploying an application, run the datagram test utility to test the actual network speed and determine its capability for pushing large amounts of data. See Chapter 4, "Performing a Network Performance Test," for details. Furthermore, the datagram test utility must be run with an increasing ratio of publishers to consumers, since a network that appears fine with a single publisher and a single consumer may completely fall apart as the number of publishers increases, as occurs with the default configuration of Cisco 6500 series switches. See "Deploying to Cisco Switches" for more information.
The term multicast refers to the ability to send a packet of information from one server and to have that packet delivered in parallel by the network to many servers. Coherence supports both multicast and multicast-free clustering. Oracle suggests the use of multicast when possible because it is an efficient option for many servers to communicate. In addition, use a known dedicated multicast address, if possible, to ensure multicast availability.
However, there are several common reasons why multicast cannot be used:
Some organizations disallow the use of multicast.
Multicast cannot operate over certain types of network equipment; for example, many WAN routers disallow or do not support multicast traffic.
Multicast is occasionally unavailable for technical reasons; for example, some switches do not support multicast traffic.
First, determine if the desired deployment configuration is to use multicast.
Before deploying an application that uses multicast, you must run the Multicast Test to verify that multicast is working and to determine the correct (the minimum) TTL value for the production environment. See Chapter 5, "Performing a Multicast Connectivity Test" for more information.
Applications that cannot use multicast for deployment must use the WKA configuration. See Oracle Coherence Developer's Guide for details.
If either the datagram test and multicast test have failed or returned poor results, there may be configuration problems with the network devices in use. Even if the tests passed without incident and the results were perfect, it is still possible that there are lurking issues with the configuration of the network devices.
Review the suggestions in "Network Tuning".
The Coherence cluster protocol can of detect and handle a wide variety of connectivity failures. The clustered services are able to identify the connectivity issue, and force the offending cluster node to leave and re-join the cluster. In this way the cluster ensures a consistent shared state among its members.
The recommendations in this sections are used to calculate the approximate size of a cache. Understanding what size cache is required can help determine how many JVMs, how much physical memory, and how many CPUs and servers are required. Hardware and JVM recommendations are provided later in this chapter. The recommendations are only guidelines: an accurate view of size can only be validated through specific tests that take into account an application's load and use cases that simulate expected users volumes, transactions profiles, processing operations, and so on.
As a general rule, allocate at least 3x the physical heap size as the data set size, assuming that you are going to keep 1 backup copy of primary data. To make a more accurate calculation, the size of a cache can be calculated as follows and also assumes 1 backup copy of primary data:
Cache Capacity = Number of entries * 2 * Entry Size
Entry Size = Serialized form of the key + Serialized form of the Value + 150 bytes
For example, consider a cache that contains 5 million objects, where the value and key serialized are 100 bytes and 2kb, respectively.
Calculate the entry size:
100 bytes + 2048 bytes + 150 bytes = 2298 bytes
Then, calculate the cache capacity:
5000000 * 2 * 2298 bytes = 21,915 MB
If indexing is used, the index size must also be taken into account. Un-ordered cache indexes consist of the serialized attribute value and the key. Ordered indexes include additional forward and backward navigation information.
Indexes are stored in memory. Each node will require 2 additional maps (instances of
java.util.HashMap) for an index: one for a reverse index and one for a forward index. The reverse index size is a cardinal number for the value (size of the value domain, that is, the number of distinct values). The forward index size is of the key set size. The extra memory cost for the
HashMap is about 30 bytes. Extra cost for each extracted indexed value is 12 bytes (the object reference size) plus the size for the value itself.
For example, the extra size for a
Long value is 20 bytes (12 bytes + 8 bytes) and for a String is 12 bytes + the string length. There is also an additional reference (12 bytes) cost for indexes with a large cardinal number and a small additional cost (about 4 bytes) for sorted indexes. Therefore, calculate an approximate index cost as:
Index size = forward index map + backward index map + reference + value size
For an indexed
Long value of large cardinal, it's going to be approximately:
30 bytes + 30 bytes + 12 bytes + 8 bytes = 80 bytes
For an indexed String of an average length of 20 chars it's going to be approximately:
30 bytes + 30 bytes + 12 bytes + (20 bytes * 2) = 112 bytes
The index cost is relatively high for small objects, but it's constant and becomes less and less expensive for larger objects.
Sizing a cache is not an exact science. Assumptions on the size and maximum number of objects have to be made. A complete example follows:
Estimated average entry size =
Estimated maximum number of cache objects =
String indexes of 20 chars =
Calculate the index size:
5 * 112 bytes * 100k = 56MB
Then, calculate the cache capacity:
100k * 2 * 1k + 56MB = ~312MB
Each JVM stores on-heap data itself and require some free space to process data. With a 1GB heap this will be approximately 300MB or more. The JVM process address space for the JVM – outside of the heap is also approximately 200MB. Therefore, to store 312MB of data requires the following memory for each node in a 2 node JVM cluster:
312MB (for data)
+ 300MB (working JVM heap)
+ 200MB (JVM executable)
= 812MB (of physical memory)
Note that this is the minimum heap space that is required. It is prudent to add additional space, to take account of any inaccuracies in your estimates, about 10%, and for growth (if this is anticipated).
With the addition of JVM memory requirements, the complete formula for calculating memory requirements for a cache can be written as follows:
Cache Memory Requirement = ((Size of cache entries + Size of indexes) * 2 (for primary and backup)) + JVM working memory (~30% of 1GB JVM)
Development typically occurs on relatively fast workstations. Moreover, test cases are usually non-clustered and tend to represent single-user access (that is, only the developer). In such environments the application may seem extraordinarily responsive.
Before moving to production, ensure that realistic load tests have been routinely run in a cluster configuration with simulated concurrent user load.
Coherence is compatible with all common workstation hardware. Most developers use PC or Apple hardware, including notebooks, desktops and workstations.
Developer systems should have a significant amount of RAM to run a modern IDE, debugger, application server, database and at least two cluster instances. Memory utilization varies widely, but to ensure productivity, the suggested minimum memory configuration for developer systems is 2GB. Desktop systems and workstations can often be configured with 4GB.
Developer systems should have two or more CPU cores to increase the quality of code related to multi-threading. Many bugs related to concurrent execution of multiple threads only appear on multi-CPU systems (systems that contain multiple processor sockets or CPU cores).
Oracle works to support the hardware that the customer has standardized on or otherwise selected for production deployment.
Oracle has customers running on virtually all major server hardware platforms. The majority of customers use "commodity x86" servers, with a significant number deploying Sun Sparc (including Niagra) and IBM Power servers.
Oracle continually tests Coherence on "commodity x86" servers, both Intel and AMD.
Intel, Apple and IBM provide hardware, tuning assistance and testing support to Oracle.
Oracle conducts internal Coherence certification on all IBM server platforms.
Oracle and Azul test Coherence regularly on Azul appliances, including the 48-core "Vega 2" chip.
If the server hardware purchase is still in the future, the following are suggested for Coherence:
The most cost-effective server hardware platform is "commodity x86", either Intel or AMD, with one to two processor sockets and two to four CPU cores per processor socket. If selecting an AMD Opteron system, it is strongly recommended that it be a two processor socket system, since memory capacity is usually halved in a single socket system. Intel "Woodcrest" and "Clovertown" Xeons are strongly recommended over the previous Intel Xeon CPUs due to significantly improved 64-bit support, much lower power consumption, much lower heat emission and far better performance. These new Xeons are currently the fastest commodity x86 CPUs, and can support a large memory capacity per server regardless of the processor socket count by using fully buffered memory called "FB-DIMMs".
It is strongly recommended that servers be configured with a minimum of 4GB of RAM. For applications that plan to store massive amounts of data in memory (tens or hundreds of gigabytes, or more), evaluate the cost-effectiveness of 16GB or even 32GB of RAM per server. Commodity x86 server RAM is readily available in a density of 2GB per DIMM, with higher densities available from only a few vendors and carrying a large price premium; so, a server with 8 memory slots only supports 16GB in a cost-effective manner. Also, note that a server with a very large amount of RAM likely must run more Coherence nodes (JVMs) per server to use that much memory, so having a larger number of CPU cores helps. Applications that are data-heavy require a higher ratio of RAM to CPU, while applications that are processing-heavy require a lower ratio. For example, it may be sufficient to have two dual-core Xeon CPUs in a 32GB server running 15 Coherence Cache Server nodes performing mostly identity-based operations (cache accesses and updates), but if an application makes frequent use of Coherence features such as indexing, parallel queries, entry processors and parallel aggregation, then it is more effective to have two quad-core Xeon CPUs in a 16GB server - a 4:1 increase in the CPU:RAM ratio.
A minimum of 1000Mbps for networking (for example, Gigabit Ethernet or better) is strongly recommended. NICs should be on a high bandwidth bus such as PCI-X or PCIe, and not on standard PCI. In the case of PCI-X having the NIC on an isolated or otherwise lightly loaded 133MHz bus may significantly improve performance. See also "Bus Considerations".
Coherence is primarily a scale-out technology. The natural mode of operation is to span many servers (for example, 2-socket or 4-socket commodity servers). However, Coherence can also effectively scale-up on a small number of large servers by using multiple JVMs per server. Failover and failback are more efficient the more servers that are present in the cluster and the impact of a server failure is lessened. A cluster must contain a minimum of four physical servers to avoid the possibility of data loss during a failure. In most WAN configurations, each data center has independent clusters (usually interconnected by Extend-TCP). This increases the total number of discrete servers (four servers per data center, multiplied by the number of data centers).
Coherence is often deployed on smaller clusters (one, two or three physical servers) but this practice has increased risk if a server failure occurs under heavy load. As discussed in "Evaluate the Production Network's Speed", Coherence clusters are ideally confined to a single switch (for example, fewer than 96 physical servers). In some use cases, applications that are compute-bound or memory-bound applications (as opposed to network-bound) may run acceptably on larger clusters.
Also, given the choice between a few large JVMs and a lot of small JVMs, the latter may be the better option. There are several production environments of Coherence that span hundreds of JVMs. Some care is required to properly prepare for clusters of this size, but smaller clusters of dozens of JVMs are readily achieved. Please note that disabling UDP multicast (by using WKA) or running on slower networks (for example, 100Mbps Ethernet) reduces network efficiency and make scaling more difficult.
The following rules should be followed in determining how many servers are required for reliable high availability configuration and how to configure the number of storage-enabled JVMs.
There must be more than two servers. A grid with only two servers stops being machine-safe as soon as several JVMs on one server are different than the number of JVMs on the other server; so, even when starting with two servers with equal number of JVMs, losing one JVM forces the grid out of machine-safe state. Four or more servers present the most stable topology, but deploying on just three servers would work if the other rules are adhered to.
For a server that has the largest number of JVMs in the cluster, that number of JVMs must not exceed the total number of JVMs on all the other servers in the cluster.
A server with the smallest number of JVMs should run at least half the number of JVMs as a server with the largest number of JVMs; this rule is particularly important for smaller clusters.
The margin of safety improves as the number of JVMs tends toward equality on all computers in the cluster; this is more of a general practice than the preceding rules.
The top three operating systems for application development using Coherence are, in this order: Windows 2000/XP (~85%), Mac OS X (~10%) and Linux (~5%). The top four operating systems for production deployment are, in this order: Linux, Solaris, AIX and Windows. Thus, it is relatively unlikely that the development and deployment operating systems are the same. Make sure that regular testing is occurring on the target operating system.
Oracle tests on and supports various Linux distributions (including customers that have custom Linux builds), Sun Solaris, IBM AIX, Windows Vista/2003/2000/XP, Apple Mac OS X, OS/400 and z/OS. Additionally, Oracle supports customers running HP-UX and various BSD UNIX distributions.
If the server operating system decision is still in the future, the following are suggested for Coherence:
For commodity x86 servers, Linux distributions based on the Linux 2.6 kernel are recommended. While it is expected that most 2.6-based Linux distributions provide a good environment for running Coherence, the following are recommended by Oracle: Oracle Unbreakable Linux supported Linux including Oracle Linux and Red Hat Enterprise Linux (version 4 or later) and Suse Linux Enterprise (version 10 or later). Oracle also routinely tests using distributions such as RedHat Fedora Core 5 and even Knoppix Live CD.
Review and follow the instructions in Chapter 2, "Platform-Specific Deployment Considerations" for the operating system on which Coherence is deployed.
In a Coherence-based application, primary data management responsibilities (for example, Dedicated Cache Servers) are hosted by Java-based processes. Modern Java distributions do not work well with virtual memory. In particular, garbage collection (GC) operations may slow down by several orders of magnitude if memory is paged to disk. With modern commodity hardware and a modern JVM, a Java process with a reasonable heap size (512MB-2GB) typically performs a full garbage collection in a few seconds if all of the process memory is in RAM. However, this may grow to many minutes if the JVM is partially resident on disk. During garbage collection, the node appears unresponsive for an extended period and the choice for the rest of the cluster is to either wait for the node (blocking a portion of application activity for a corresponding amount of time) or to consider the unresponsive node as failed and perform failover processing. Neither of these outcomes are a good option, and it is important to avoid excessive pauses due to garbage collection. JVMs should be configured with a set heap size to ensure that the heap does not deplete the available RAM memory. Also, periodic processes (such as daily backup programs) should be monitored to ensure that memory usage spikes do not cause Coherence JVMs to be paged to disk.
See also: "Swapping".
During development, developers typically use the latest Oracle JVM or a direct derivative such as the Mac OS X JVM.
The main issues related to using a different JVM in production are:
Command line differences, which may expose problems in shell scripts and batch files;
Logging and monitoring differences, which may mean that tools used to analyze logs and monitor live JVMs during development testing may not be available in production;
Significant differences in optimal GC configuration and approaches to GC tuning;
Differing behaviors in thread scheduling, garbage collection behavior and performance, and the performance of running code.
Make sure that regular testing has occurred on the JVM that is used in production.
In terms of Oracle Coherence versions:
Coherence is supported on Oracle JVM 1.6 update 23.
Often the choice of JVM is dictated by other software. For example:
IBM only supports IBM WebSphere running on IBM JVMs. Most of the time, this is the IBM "Sovereign" or "J9" JVM, but when WebSphere runs on Sun Solaris/Sparc, IBM builds a JVM using the Sun JVM source code instead of its own.
Oracle WebLogic typically includes a JVM which is intended to be used with it. On some platforms, this is the Oracle WebLogic JRockit JVM.
Apple Mac OS X, HP-UX, IBM AIX and other operating systems only have one JVM vendor (Apple, HP and IBM respectively).
Certain software libraries and frameworks have minimum Java version requirements because they take advantage of relatively new Java features.
On commodity x86 servers running Linux or Windows, the Oracle JVM is recommended. Generally speaking, the recent update versions are recommended.
Note:Oracle recommends testing and deploying using the latest supported Oracle JVM based on your platform and Coherence version.
Basically, at some point before going to production, a JVM vendor and version should be selected and well tested, and absent any flaws appearing during testing and staging with that JVM, that should be the JVM that is used when going to production. For applications requiring continuous availability, a long-duration application load test (for example, at least two weeks) should be run with that JVM before signing off on it.
Review and follow the instructions in Chapter 2, "Platform-Specific Deployment Considerations" for the JVM on which Coherence is deployed.
JVM configuration options vary over versions and between vendors, but the following are generally suggested. See Chapter 2, "Platform-Specific Deployment Considerations," for specific JVM considerations.
-server option results in substantially better performance.
Using identical heap size values for both
-Xmx yields substantially better performance on Oracle and JRockit JVMs and "fail fast" memory allocation.
Monitor garbage collection– especially when using large heaps:
JVMs that experience an
OutOfMemoryError can be left in an indeterministic state which can have adverse effects on a cluster. We recommend configuring JVMs to exit upon encountering an
OutOfMemoryError instead of allowing the JVM to attempt recovery: On Linux,
-XX:OnOutOfMemoryError="kill -9 %p"; on Windows,
-XX:OnOutOfMemoryError="taskkill /F /PID %p".
Capture a heap dump if the JVM experiences an out of memory error:
Coherence is pure Java software and can run in clusters composed of any combination of JVM vendors and versions and Oracle tests such configurations.
Note that it is possible for different JVMs to have slightly different serialization formats for Java objects, meaning that it is possible for an incompatibility to exist when objects are serialized by one JVM, passed over the wire, and a different JVM (vendor, version, or both) attempts to deserialize it. Fortunately, the Java serialization format has been very stable for several years, so this type of issue is extremely unlikely. However, it is highly recommended to test mixed configurations for consistent serialization before deploying in a production environment.
The minimum set of privileges required for Coherence to function are specified in the
security.policy file which is included as part of the Coherence installation. This file can be found in
coherence/lib/security/security.policy. If using the Java Security Manager, these privileges must be granted in order for Coherence to function properly.
Coherence-based applications may chose to implement varying levels of security as required, including SSL-based security between cluster members and between Coherence*Extend clients and the cluster. If SSL is a requirement, ensure that all servers have a digital certificate that has been verified and signed by a trusted certificate authority and that the digital certificate is imported into the servers' key store and trust store as required. Coherence*Extend clients must include a trust key store that contains the certificate authority's digital certificate that was used to sign the proxy's digital certificate. See Oracle Coherence Security Guide for detailed instructions on setting up SSL.
Some Java-based management and monitoring solutions use instrumentation (for example, bytecode-manipulation and
ClassLoader substitution). Oracle has observed issues with such solutions in the past. Use these solutions cautiously even though there are no current issues reported with the major vendors.
Coherence may be configured to operate in either evaluation, development, or production mode. These modes do not limit access to features, but instead alter some default configuration settings. For instance, development mode allows for faster cluster startup to ease the development process.
The development mode is used for all pre-production activities, such as development and testing. This is an important safety feature because development nodes are restricted from joining with production nodes. Development mode is the default mode. Production mode must be explicitly specified when using Coherence in a production environment. To change the mode to production mode, edit the
tangosol-coherence.xml (located in
coherence.jar) and enter
prod as the value for the
<license-mode> element. For example:
... <license-config> ... <license-mode system-property="tangosol.coherence.mode">prod</license-mode> </license-config> ...
tangosol.coherence.mode system property is used to specify the license mode instead of using the operational deployment descriptor. For example:
In addition to preventing mixed mode clustering, the
license-mode also dictates the operational override file to use. When in
eval mode the
tangosol-coherence-override-eval.xml file is used; when in
dev mode the
tangosol-coherence-override-dev.xml file is used; whereas, the
tangosol-coherence-override-prod.xml file is used when the
prod mode is specified. A
tangosol-coherence-override.xml file (if it is included in the classpath before the
coherence.jar file) is used no matter which mode is selected and overrides any mode-specific override files.
Note:The current recommendation is not to use the edition switch. The switches are no longer used to enforce license restrictions.
All nodes within a cluster must use the same license edition and mode. Be sure to obtain enough licenses for the all the cluster members in the production environment. The servers hardware configuration (number or type of processor sockets, processor packages or CPU cores) may be verified using
ProcessorInfo utility included with Coherence. For example:
java -cp coherence.jar com.tangosol.license.ProcessorInfo
If the result of the
ProcessorInfo program differs from the licensed configuration, send the program's output and the actual configuration as a support issue.
The default edition is grid edition. To change the edition, edit the operational override file and add an
<edition-name> element, within the
<license-config> element, that includes an edition name as defined in Table 7-1. For example:
... <license-config> <edition-name system-property="tangosol.coherence.edition">EE</edition-name> </license-config> ...
tangosol.coherence.edition system property is used to specify the license edition instead of using the operational deployment descriptor. For example:
|Value||Coherence Edition||Compatible Editions|
GE, EE, SE
Note:clusters running different editions may connect by using Coherence*Extend as a Data Client.
Real-Time client nodes can connect to clusters using either Coherence TCMP or Coherence*Extend. If the intention is to use extend clients, Disable TCMP on the client to ensure that it only connects to a cluster using Coherence*Extend. Otherwise, The client may become a member of the cluster. See Oracle Coherence Client Guide for details on disabling TCMP communication.
Operational configuration relates to cluster-level configuration that is defined in the
tangosol-coherence.xml file and includes such items as:
Cluster and cluster member settings
The operational aspects are typically configured by using a
tangosol-coherence-override.xml file. See Oracle Coherence Developer's Guide for more information on specifying an operational override file.
The contents of this file often differs between development and production. It is recommended that these variants be maintained independently due to the significant differences between these environments. The production operational configuration file should be maintained by systems administrators who are far more familiar with the workings of the production systems.
All cluster nodes should use the same operational configuration override file and any node-specific values should be specified by using system properties. See Oracle Coherence Developer's Guide for more information on system properties. A centralized configuration file may be maintained and accessed by specifying a URL as the value of the
tangosol.coherence.override system property on each cluster node. For example:
The override file need only contain the operational elements that are being changed. In addition, always include the
system-property attributes if they are defined for an element.
See Oracle Coherence Developer's Guide for a detailed reference of each operational element.
Cache configuration relates to cache-level configuration and includes such things as:
Cache topology (
<near-scheme>, and so on)
Cache capacities (
Cache redundancy level (
The cache configuration aspects are typically configured by using a
coherence-cache-config.xml file. See Oracle Coherence Developer's Guide for more information on specifying a cache configuration file.
coherence-cache-config.xml file included within
coherence.jar is intended only as an example and is not suitable for production use. Always use a cache configuration file with definitions that are specific to the application.
All cluster nodes should use the same cache configuration descriptor if possible. A centralized configuration file may be maintained and accessed by specifying a URL as the value the
tangosol.coherence.cacheconfig system property on each cluster node. For example:
Always configure caches to have a size limit based on the allocated JVM heap size. The limits protect an application from
OutOfMemoryExceptions errors. Set the limits even if the cache is not expected to be fully loaded to protect against changing expectations. See "JVM Tuning" for sizing recommendations.
It is important to note that when multiple cache schemes are defined for the same cache service name, the first to be loaded dictates the service level parameters. Specifically the
<thread-count> subelements of
<distributed-scheme> are shared by all caches of the same service. it is recommended that a single service be defined and inherited by the various cache-schemes. If you want different values for these items on a cache by cache basis then multiple services may be configured.
For partitioned caches, Coherence evenly distributes the storage responsibilities to all cache servers, regardless of their cache configuration or heap size. For this reason, it is recommended that all cache server processes be configured with the same heap size. For computers with additional resources multiple cache servers may be used to effectively make use of the computer's resources.
To ensure even storage responsibility across a partitioned cache the
<partition-count> subelement of the
<distributed-scheme> element, should be set to a prime number which is at least the square of the number of expected cache servers.
For caches which are backed by a cache store, it is recommended that the parent service be configured with a thread pool as requests to the cache store may block on I/O. The pool is enabled by using the
<thread-count> subelement of
<distributed-scheme> element. For non-CacheStore-based caches more threads are unlikely to improve performance and should be left disabled.
Unless explicitly specified, all cluster nodes are storage enabled, that is, they act as cache servers. It is important to control which nodes in your production environment are storage enabled and storage disabled. The
tangosol.coherence.distributed.localstorage system property may be used to control storage, setting it to either
false. Generally, only dedicated cache servers should have storage enabled. All other cluster nodes should be configured as storage disabled. This is especially important for short lived processes which may join the cluster to perform some work and then exit the cluster. Having these nodes as storage enabled introduces unneeded re-partitioning.
See Oracle Coherence Developer's Guide for a detailed reference of each cache configuration element.
The general recommendation for the
<partition-count> subelement of the
<distributed-scheme> element is to be a prime number close to the square of the number of storage enabled nodes. This formula is not ideal for large clusters because it adds too much overhead. For clusters exceeding 128 storage enabled JVMs, the partition count should be fixed, at roughly 16,381.
Coherence clusters which consist of over 400 TCMP nodes must increase the default maximum packet size that Coherence uses. The default of 1468 should be increased relative to the size of the cluster, that is, a 600 node cluster would need the maximum packet size increased by 50%. A simple formula is to allow four bytes per node, that is,
maximum_packet_size >= maximum_cluster_size * 4B. The maximum packet size is configured as part of the coherence operational configuration file, see Oracle Coherence Developer's Guide for details on configuring the maximum packet size.
For large clusters which have hundreds of JVMs, it is also recommended that multicast be enabled because it provides more efficient cluster-wide transmissions. These cluster-wide transmissions are rare, but when they do occur multicast can provide noticeable benefits in large clusters.
The Coherence death detection algorithms are based on sustained loss of connectivity between two or more cluster nodes. When a node identifies that it has lost connectivity with any other node, it consults with other cluster nodes to determine what action should be taken.
In attempting to consult with others, the node may find that it cannot communicate with any other nodes and assumes that it has been disconnected from the cluster. Such a condition could be triggered by physically unplugging a node's network adapter. In such an event, the isolated node restarts it's clustered services and attempts to rejoin the cluster.
If connectivity with other cluster nodes remains unavailable, the node may (depending on well known address configuration) form a new isolated cluster, or continue searching for the larger cluster. In either case, the previously isolated cluster nodes rejoins the running cluster when connectivity is restored. As part of rejoining the cluster, the nodes former cluster state is discarded, including any cache data it may have held, as the remainder of the cluster has taken on ownership of that data (restoring from backups).
It is obviously not possible for a node to identify the state of other nodes without connectivity. To a single node, local network adapter failure and network wide switch failure looks identical and are handled in the same way, as described above. The important difference is that for a switch failure all nodes are attempting to re-join the cluster, which is the equivalent of a full cluster restart, and all prior state and data is dropped.
Dropping all data is not desirable and, to avoid this as part of a sustained switch failure, you must take additional precautions. Options include:
Increase detection intervals: The cluster relies on a deterministic process-level death detection using the TcpRing component and hardware death detection using the IpMonitor component. Process-level detection is performed within milliseconds and network or machine failures are detected within 15 seconds by default. Increasing these value allows the cluster to wait longer for connectivity to return. Death detection is enabled by default and is configured within the
<tcp-ring-listener> element. See Oracle Coherence Developer's Guide for details on configuring death detection.
Persist data to external storage: By using a Read Write Backing Map, the cluster persists data to external storage, and can retrieve it after a cluster restart. So long as write-behind is disabled (the
<write-delay> subelement of
<read-write-backing-map-scheme>) no data would be lost if a switch fails. The downside here is that synchronously writing through to external storage increases the latency of cache update operations, and the external storage may become a bottleneck.
Decide on a cluster quorum: The cluster quorum policy mandates the minimum number of cluster members that must remain in the cluster when the cluster service is terminating suspect members. During intermittent network outages, a high number of cluster members may be removed from the cluster. Using a cluster quorum, a certain number of members are maintained during the outage and are available when the network recovers. See Oracle Coherence Developer's Guide for details on configuring cluster quorum.
Note:To ensure that Windows does not disable a network adapter when it is disconnected, add the following Windows registry
DWORD, setting it to
1:HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\Tcpip\Parameters\DisableDHCPMediaSense. This setting also affects static IPs despite the name.
Add network level fault tolerance: Adding a redundant layer to the cluster's network infrastructure allows for individual pieces of networking equipment to fail without disrupting connectivity. This is commonly achieved by using at least two network adapters per computer, and having each adapter connected to a separate switch. This is not a feature of Coherence but rather of the underlying operating system or network driver. The only change to Coherence is that it should be configured to bind to the virtual rather then physical network adapter. This form of network redundancy goes by different names depending on the operating system, see Linux bonding, Solaris trunking and Windows teaming for further details.