@IgniteSpiMultipleInstancesSupport(value=true) public class AdaptiveLoadBalancingSpi extends IgniteSpiAdapter implements LoadBalancingSpi
AdaptiveLoadProbe
interface and user is
free to provide custom implementations. By default
AdaptiveCpuLoadProbe
implementation is used
which distributes jobs to nodes based on average CPU load
on every node.
The following load probes are available with the product:
Note that ifAdaptiveLoadProbe.getLoad(org.apache.ignite.cluster.ClusterNode, int)
returns a value of 0
,
then implementation will assume that load value is simply not available and
will try to calculate an average of load values for other nodes. If such
average cannot be obtained (all node load values are 0
), then a value
of 1
will be used.
When working with node metrics, take into account that all averages are
calculated over metrics history size defined by IgniteConfiguration.getMetricsExpireTime()
and IgniteConfiguration.getMetricsHistorySize()
grid configuration parameters.
Generally the larger these configuration parameter values are, the more precise the metrics are.
You should tune these values based on the level of accuracy needed vs. the additional memory
that would be required for storing metrics.
You should also keep in mind that metrics for remote nodes are delayed (usually by the metrics
update frequency). So if it is acceptable in your environment, set the metrics update frequency
to be more inline with job execution time. Generally, the more often metrics update between nodes
are exchanged, the more precise the metrics are. However, you should keep in mind that if
metrics update are exchanged too often then it may create unnecessary traffic in the network.
Metrics update frequency can be configured via underlying
IgniteConfiguration
used in your grid.
Here is an example of how probing can be implemented to use number of active and waiting jobs as probing mechanism:
public class FooBarLoadProbe implements GridAdaptiveLoadProbe { // Flag indicating whether to use average value or current. private int useAvg = true; public FooBarLoadProbe(boolean useAvg) { this.useAvg = useAvg; } // Calculate load based on number of active and waiting jobs. public double getLoad(ClusterNode node, int jobsSentSinceLastUpdate) { GridNodeMetrics metrics = node.getMetrics(); if (useAvg) { double load = metrics.getAverageActiveJobs() + metrics.getAverageWaitingJobs(); if (load > 0) { return load; } } return metrics.getCurrentActiveJobs() + metrics.getCurrentWaitingJobs(); } }
ComputeTaskSplitAdapter
then load balancing logic
is transparent to your code and is handled automatically by the adapter.
Here is an example of how your task will look:
public class MyFooBarTask extends ComputeTaskSplitAdapter<Object, Object> { @Override protected Collection<? extends ComputeJob> split(int gridSize, Object arg) throws IgniteCheckedException { List<MyFooBarJob> jobs = new ArrayList<MyFooBarJob>(gridSize); for (int i = 0; i < gridSize; i++) { jobs.add(new MyFooBarJob(arg)); } // Node assignment via load balancer // happens automatically. return jobs; } ... }If you need more fine-grained control over how some jobs within task get mapped to a node and use affinity load balancing for some other jobs within task, then you should use
ComputeTaskAdapter
. Here is an example of how your task will look. Note that in this
case we manually inject load balancer and use it to pick the best node. Doing it in
such way would allow user to map some jobs manually and for others use load balancer.
public class MyFooBarTask extends ComputeTaskAdapter<String, String> { // Inject load balancer. @LoadBalancerResource ComputeLoadBalancer balancer; // Map jobs to grid nodes. public Map<? extends ComputeJob, ClusterNode> map(List<ClusterNode> subgrid, String arg) throws IgniteCheckedException { Map<MyFooBarJob, ClusterNode> jobs = new HashMap<MyFooBarJob, ClusterNode>(subgrid.size()); // In more complex cases, you can actually do // more complicated assignments of jobs to nodes. for (int i = 0; i < subgrid.size(); i++) { // Pick the next best balanced node for the job. jobs.put(new MyFooBarJob(arg), balancer.getBalancedNode()) } return jobs; } // Aggregate results into one compound result. public String reduce(List<ComputeJobResult> results) throws IgniteCheckedException { // For the purpose of this example we simply // concatenate string representation of every // job result StringBuilder buf = new StringBuilder(); for (ComputeJobResult res : results) { // Append string representation of result // returned by every job. buf.append(res.getData().string()); } return buf.string(); } }
JobsLoadBalancingSpi
either from Spring XML file or
directly. The following configuration parameters are supported:
setLoadProbe(AdaptiveLoadProbe)
).
This configuration parameter supplies a custom algorithm for probing a node's load.
By default, AdaptiveCpuLoadProbe
implementation is used which
takes every node's CPU load and tries to send proportionally more jobs to less loaded nodes.
Below is Java configuration example:
AdaptiveLoadBalancingSpi spi = new AdaptiveLoadBalancingSpi(); // Configure probe to use latest job execution time vs. average. AdaptiveProcessingTimeLoadProbe probe = new AdaptiveProcessingTimeLoadProbe(false); spi.setLoadProbe(probe); IgniteConfiguration cfg = new IgniteConfiguration(); // Override default load balancing SPI. cfg.setLoadBalancingSpi(spi); // Starts grid. G.start(cfg);Here is how you can configure
GridJobsLoadBalancingSpi
using Spring XML configuration:
<property name="loadBalancingSpi"> <bean class="org.apache.ignite.spi.loadBalancing.adaptive.AdaptiveLoadBalancingSpi"> <property name="loadProbe"> <bean class="org.apache.ignite.spi.loadBalancing.adaptive.AdaptiveProcessingTimeLoadProbe"> <constructor-arg value="false"/> </bean> </property> </bean> </property>
For information about Spring framework visit www.springframework.org
ignite, igniteInstanceName
Constructor and Description |
---|
AdaptiveLoadBalancingSpi() |
Modifier and Type | Method and Description |
---|---|
ClusterNode |
getBalancedNode(ComputeTaskSession ses,
List<ClusterNode> top,
ComputeJob job)
Gets balanced node for specified job within given task session.
|
String |
getLoadProbeFormatted()
Gets text description of current load probing implementation used.
|
protected void |
onContextDestroyed0()
Method to be called in the beginning of onContextDestroyed() method.
|
protected void |
onContextInitialized0(IgniteSpiContext spiCtx)
Method to be called in the end of onContextInitialized method.
|
AdaptiveLoadBalancingSpi |
setLoadProbe(AdaptiveLoadProbe probe)
Sets implementation of node load probe.
|
AdaptiveLoadBalancingSpi |
setName(String name)
Sets SPI name.
|
void |
spiStart(@Nullable String igniteInstanceName)
This method is called to start SPI.
|
void |
spiStop()
This method is called to stop SPI.
|
String |
toString() |
addTimeoutObject, assertParameter, checkConfigurationConsistency0, clientFailureDetectionTimeout, configInfo, createSpiAttributeName, failureDetectionTimeout, failureDetectionTimeoutEnabled, failureDetectionTimeoutEnabled, getConsistentAttributeNames, getExceptionRegistry, getLocalNode, getName, getNodeAttributes, getSpiContext, ignite, initFailureDetectionTimeout, injectables, injectResources, isNodeStopping, onBeforeStart, onClientDisconnected, onClientReconnected, onContextDestroyed, onContextInitialized, registerMBean, removeTimeoutObject, started, startInfo, startStopwatch, stopInfo, unregisterMBean
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
getName, getNodeAttributes, onClientDisconnected, onClientReconnected, onContextDestroyed, onContextInitialized
public String getLoadProbeFormatted()
@IgniteSpiConfiguration(optional=true) public AdaptiveLoadBalancingSpi setLoadProbe(AdaptiveLoadProbe probe)
AdaptiveProcessingTimeLoadProbe
is used which proportionally distributes load based on the average job execution
time on every node.probe
- Implementation of node load probethis
for chaining.public void spiStart(@Nullable @Nullable String igniteInstanceName) throws IgniteSpiException
spiStart
in interface IgniteSpi
igniteInstanceName
- Name of Ignite instance this SPI is being started for
(null
for default Ignite instance).IgniteSpiException
- Throws in case of any error during SPI start.public void spiStop() throws IgniteSpiException
Note that this method can be called at any point including during recovery of failed start. It should make no assumptions on what state SPI will be in when this method is called.
spiStop
in interface IgniteSpi
IgniteSpiException
- Thrown in case of any error during SPI stop.protected void onContextInitialized0(IgniteSpiContext spiCtx) throws IgniteSpiException
onContextInitialized0
in class IgniteSpiAdapter
spiCtx
- SPI context.IgniteSpiException
- In case of errors.protected void onContextDestroyed0()
onContextDestroyed0
in class IgniteSpiAdapter
public ClusterNode getBalancedNode(ComputeTaskSession ses, List<ClusterNode> top, ComputeJob job)
getBalancedNode
in interface LoadBalancingSpi
ses
- Grid task session for currently executing task.top
- Topology of task nodes from which to pick the best balanced node for given job.job
- Job for which to pick the best balanced node.public AdaptiveLoadBalancingSpi setName(String name)
setName
in class IgniteSpiAdapter
name
- SPI name.this
for chaining.
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024