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Add documentation for changing load manager (#325)
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bobbeyreese authored and rdhabalia committed May 2, 2017
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- [Geo-Replication](GeoReplication.md)
- [WebSocket API](WebSocket.md)
- [Apache Storm adaptor](PulsarStorm.md)
- [Modular Load Manager](ModularLoadManager.md)
* Internal Docs
- [Binary protocol specification](BinaryProtocol.md)
* Other Languages
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112 changes: 112 additions & 0 deletions docs/ModularLoadManager.md
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# Modular Load Manager
The modular load manager, implemented in `ModularLoadManagerImpl`, is a flexible alternative to the previously
implemented load manager `SimpleLoadManagerImpl` which attempts to simplify how load is manager while also providing
abstractions so that complex load management strategies may be implemented.

## Usage
To use the modular load manager, change

`loadManagerClassName=com.yahoo.broker.loadbalance.impl.SimpleLoadManagerImpl`

in `broker.conf` to

`loadManagerClassName=com.yahoo.broker.loadbalance.impl.ModularLoadManagerImpl`

Alternatively, the load manager may also be changed dynamically via the `pulsar-admin` tool as follows:

`pulsar-admin update-dynamic-config --config loadManagerClassName --value
com.yahoo.broker.loadbalance.impl.ModularLoadManagerImpl`

The admin tool may also be used to change back to `com.yahoo.broker.loadbalance.impl.SimpleLoadManagerImpl`. In either
case, any mistake in specifying the load manager will cause Pulsar to default to `SimpleLoadManagerImpl`.

## Implementation

### Data
The data monitored by the modular load manager is contained in the class `com.yahoo.pulsar.broker.loadbalance.LoadData`.
Here, the available data is subdivided into the bundle data and the broker data.

#### Broker Data
The broker data is contained in the class `com.yahoo.pulsar.broker.BrokerData`. It is further subdivided into two parts,
one being the local data which every broker individually writes to ZooKeeper, and the other being the historical broker
data which is written to ZooKeeper by the leader broker.

##### Local Broker Data
The local broker data is contained in the class
`com.yahoo.pulsar.broker.LocalBrokerData` and gives information about the following resources:

* CPU usage
* JVM heap memory usage
* Direct memory usage
* Bandwidth in/out usage
* Most recent total message rate in/out across all bundles
* Total number of topics, bundles, producers, and consumers
* Names of all bundles assigned to this broker
* Most recent changes in bundle assignments for this broker

The local broker data is updated periodically according to the service configuration
"loadBalancerReportUpdateMaxIntervalMinutes". After any broker updates their local broker data, the leader broker will
receive the update immediately via a ZooKeeper watch, where the local data is read from the ZooKeeper node
`/loadbalance/brokers/<broker host/port>`

##### Historical Broker Data
The historical broker data is contained in the class `com.yahoo.pulsar.broker.TimeAverageBrokerData`.
In order to reconcile the need to make good decisions in a steady-state scenario and make reactive decisions in a
critical scenario, the historical data is split into two parts: the short-term data for reactive decisions, and the
long-term data for steady-state decisions. Both time frames maintain the following information:

* Message rate in/out for the entire broker
* Message throughput in/out for the entire broker

Unlike the bundle data, the broker data does not maintain samples for the global broker message rates and throughputs,
which is not expected to remain steady as new bundles are removed or added. Instead, this data is aggregated over the
short-term and long-term data for the bundles. See the section on bundle data to understand how that data is collected
and maintained. The historical broker data is updated for each broker in memory by the leader broker whenever any broker
writes their local data to ZooKeeper. Then, the historical data is written to ZooKeeper by the leader broker
periodically according to the configuration `loadBalancerResourceQuotaUpdateIntervalMinutes`.

##### Bundle Data
The bundle data is contained in the class `com.yahoo.pulsar.broker.BundleData`.
Like the historical broker data, the bundle data is split into a short-term and a long-term time frame.
The information maintained in each time frame is

* Message rate in/out for this bundle
* Message Throughput In/Out for this bundle
* Current number of samples for this bundle

The time frames are implemented by maintaining the average of these values over a set, limited number of samples, where
the samples are obtained through the message rate and throughput values in the local data. Thus, if the update interval
for the local data is 2 minutes, the number of short samples is 10 and the number of long samples is 1000, the
short-term data is maintained over a period of `10 samples * 2 minutes / sample = 20 minutes`, while the long-term
data is similarly over a period of 2000 minutes. Whenever there are not enough samples to satisfy a given time frame,
the average is taken only over the existing samples. When no samples are available, default values are assumed until
they are overwritten by the first sample. Currently, the default values are

* Message rate in/out: 50 messages per second both ways
* Message throughput in/out: 50KB per second both ways

The bundle data is updated in memory on the leader broker whenever any broker writes their local data to ZooKeeper.
Then, the bundle data is written to ZooKeeper by the leader broker periodically at the same time as the historical
broker data, according to the configuration `loadBalancerResourceQuotaUpdateIntervalMinutes`.

### Traffic Distribution
The modular load manager uses the abstraction provided by
`com.yahoo.pulsar.broker.loadbalance.ModularLoadManagerStrategy` to make decisions about bundle assignment. The
strategy makes a decision by considering the service configuration, the entire load data, and the bundle data for the
bundle to be assigned. Currently, the only supported strategy is
`com.yahoo.pulsar.broker.loadbalance.impl.LeastLongTermMessageRate`, though soon users will have the ability to inject
their own strategies if desired.

#### Least Long Term Message Rate Strategy
As its name suggests, the least long term message rate strategy attempts to distribute bundles across brokers so that
the message rate in the long-term time window for each broker is roughly the same. However, simply balancing load based
on message rate does not handle the issue of asymmetric resource burden per message on each broker. Thus, the system
resource usages, which are CPU, memory, direct memory, bandwidth in, and bandwidth out, are also considered in the
assignment process. This is done by weighting the final message rate according to
`1 / (overload_threshold - max_usage)`, where `overload_threshold` corresponds to the configuration
`loadBalancerBrokerOverloadedThresholdPercentage` and `max_usage` is the maximum proportion among the system resources
that is being utilized by the candidate broker. This multiplier ensures that machines with are being more heavily taxed
by the same message rates will receive less load. In particular, it tries to ensure that if one machine is overloaded,
then all machines are approximately overloaded. In the case in which a broker's max usage exceeds the overload
threshold, that broker is not considered for bundle assignment. If all brokers are overloaded, the bundle is randomly
assigned.

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