Performance Tuning

This document goes over various tips and configuration to tune Alluxio performance.

Common Performance Issues

The following is a checklist to run through to address common problems when tuning performance:

1. Are all nodes working?

   Check that the Alluxio cluster is healthy. You can check the web user interface at http://MasterHost:19999 to see if the masters and workers are working correctly from a browser. Alternatively, you can run bin/alluxio fsadmin report to collect similar information from the console. Important metrics to verify if any nodes are out of service are the number of lost workers and the last heartbeat time.

2. Are short-circuit operations working?

   If the compute application is running co-located with Alluxio workers, check that the application is performing short-circuit reads and writes with its local Alluxio worker. Monitor the metrics values for cluster.BytesReadAlluxioThroughput and cluster.BytesReadLocalThroughput while the application is running (Metrics can be viewed through alluxio fsadmin report metrics. ). If the local throughput is zero or significantly lower than the total throughput, the compute application is likely not interfacing with a local Alluxio worker. The Alluxio client uses hostname matching to discover a local Alluxio worker; check that the client and worker use the same hostname string. Configuring alluxio.user.hostname and alluxio.worker.hostname sets the client and worker hostnames respectively.

   Note: In order to retrieve metrics for short circuit IO, the client metrics collection need to be enabled by setting alluxio.user.metrics.collection.enabled=true in alluxio-site.properties or corresponding application configuration.

3. Is data is well-distributed across Alluxio workers?

   By default, Alluxio clients will use the LocalFirstPolicy to write data to their local Alluxio worker. This is efficient for applications which write data from many nodes concurrently. In a scenario where all data is written from a single node, its local worker will be filled, leaving the remaining workers empty. See this page for discussion of the different location policies and how to configure them.

4. Are there error messages containing “DeadlineExceededException” in the user logs?

   This could indicate that the client is timing out when communicating with the Alluxio worker. To increase the timeout, configure alluxio.user.network.data.timeout, which has a default of 30s.

   If write operations are timing out, configure alluxio.user.network.writer.close.timeout, which has a default of 30m. This is especially important when writing large files to object stores with a slow network connection. The entire object is uploaded at once upon closing the file.

5. Are there frequent JVM GC events?

   Frequent and long GC operations on master or worker JVMs drastically slow down the process. This can be identified by adding logging for GC events; append the following to conf/allulxio-env.sh:

ALLUXIO_JAVA_OPTS=" -XX:+PrintGCDetails -XX:+PrintTenuringDistribution -XX:+PrintGCTimeStamps"

Restart the Alluxio servers and check the output in logs/master.out or logs/worker.out for masters and workers respectively.

Also check out the metrics system for better insight in how the Alluxio service is performing.

General Tuning

JVM Monitoring

To detect long GC pauses, Alluxio administrators can set alluxio.master.jvm.monitor.enabled=true for masters or alluxio.worker.jvm.monitor.enabled=true for workers. This will trigger a monitoring thread that periodically measures the delay between two GC pauses. A long delay could indicate that the process is spending significant time garbage collecting. The following parameters tune the behavior of the monitor thread:

Improve Cold Read Performance

When the application reads directly from the UFS, multiple clients may try to read the same portion of the input data simultaneously. For example, at the start of a SparkSQL query, all Spark executors will read the same parquet footer metadata. This potentially results in Alluxio caching the same block on every node, which is potentially a waste of both UFS bandwidth and Alluxio storage capacity.

One way to avoid this situation is to apply a deterministic hashing policy by specifying the following configuration property:

alluxio.user.ufs.block.read.location.policy=alluxio.client.block.policy.DeterministicHashPolicy

This will cause Alluxio to select a single random worker to read the given block from the UFS and cause any other clients requesting the same block to instead read from the selected worker. To increase the number of workers allowed to simultaneously read the same block from the UFS, update the following configuration property to a value greater than the default of 1:

alluxio.user.ufs.block.read.location.policy.deterministic.hash.shards=3

Setting this to 3 means there will be 3 Alluxio workers responsible for reading a particular UFS block, and all clients will read that UFS block from one of those 3 workers.

Master Tuning

Journal performance tuning

Increasing the batch time can improve master throughput for update/write RPCs, but may also increase the latency for those update/write RPCs. Setting a larger timeout value helps keep the master alive if the journal writing location is unavailable for an extended duration.

Journal garbage collection

Journal checkpoints are expensive to create, but decrease startup time by reducing the number of journal entries that the master needs to process during startup. If startup is taking too long, consider reducing this value so that checkpoints happen more often.

UFS block locations cache

The Alluxio client provides block locations, similar to the HDFS client. If a file block is not stored in Alluxio, Alluxio will consult the UFS for its block locations, requiring an additional RPC. This extra overhead can be avoided by caching the UFS block locations. The size of this cache is determined by the value of alluxio.master.ufs.block.location.cache.capacity. Caching is disabled if the value is set to 0.

Increasing the cache size will allow the Alluxio master to store more UFS block locations, leading to greater metadata throughput for files which are not residing in Alluxio storage.

UFS Path Cache

When Alluxio mounts a UFS to a path in the Alluxio namespace, the Alluxio master maintains metadata on its namespace. The UFS metadata is only pulled when a client accesses a path. When a client accesses a path which does not exist in Alluxio, Alluxio may consult the UFS to load the UFS metadata. There are 3 options for loading a missing path: Never, Once, Always.

ALWAYS will always check the UFS for the latest state of the given path, ONCE will use the default behavior of only scanning each directory once ever, and NEVER will never consult the UFS and thus prevent Alluxio from scanning for new files at all.

The Alluxio master maintains a cache to keep track of which UFS paths have been previously loaded, to approximate the Once behavior. The parameter alluxio.master.ufs.path.cache.capacity controls the number of paths to store in the cache. A larger cache size will consume more memory, but will better approximate the Once behavior. The Alluxio master maintains the UFS path cache asynchronously. Alluxio uses a thread pool to process the paths asynchronously, whose size is controlled by alluxio.master.ufs.path.cache.threads. Increasing the number of threads can decrease the staleness of the UFS path cache, but may impact performance by increasing work on the Alluxio master, as well as consuming UFS bandwidth. If this is set to 0, the cache is disabled and the Once setting will behave like the Always setting.

Worker Tuning

Block reading thread pool size

The alluxio.worker.network.block.reader.threads.max property configures the maximum number of threads used to handle block read requests. This value should be increased if you are getting connection refused errors while reading files.

Async block caching

When a worker requests for data from a portion of a block, the worker reads as much data as requested and immediately returns the requested data to the client. The worker will asynchronously continue to read the remainder of the block without blocking the client request.

The number of asynchronous threads used to finish reading partial blocks is set by the alluxio.worker.network.async.cache.manager.threads.max property. When large amounts of data are expected to be asynchronously cached concurrently, it may be helpful to increase this value to handle a higher workload. This is most commonly effective in cases where the files being cached are relatively small (> 10MB). However, increase this number sparingly, as it will consume more CPU resources on the worker node as the number is increased.

UFS InStream cache size

Alluxio workers use a pool of open input streams to the UFS controlled by the parameter alluxio.worker.ufs.instream.cache.max.size. A high number reduces the overhead of opening a new stream to the UFS. However, it also places greater load on the UFS. For HDFS as the UFS, the parameter should be set based on dfs.datanode.handler.count. For instance, if the number of Alluxio workers matches the the number of HDFS datanodes, set alluxio.worker.ufs.instream.cache.max.size=<value of HDFS setting dfs.datanode.handler.count> under the assumption that the workload is spread evenly over Alluxio workers.

Client Tuning

Passive caching

Passive caching causes an Alluxio worker to cache another copy of data already cached on a separate worker. Passive caching is disabled by setting the configuration property:

alluxio.user.file.passive.cache.enabled=false

When enabled, the same data blocks are available across multiple workers, reducing the amount of available storage capacity for unique data. Disabling passive caching is important for workloads that have no concept of locality and whose dataset is large compared to the capacity of a single Alluxio worker.

Optimized Commits for Compute Frameworks

Running with optimized commits through Alluxio can provide an order of magnitude improvement in the overall runtime of compute jobs.

Computation frameworks that leverage the Hadoop MapReduce committer pattern (ie. Spark, Hive) are not optimally designed for interacting with storages that provide slow renames (mainly Object Stores). This is most common when using stacks such as Spark on S3 or Hive on Ceph.

The Hadoop MapReduce committer leverages renames to commit data from a staging directory (usually output/_temporary) to the final output directory (ie. output). When writing data with CACHE_THROUGH or THROUGH this protocol translates to the following:

1. Write temporary data to Alluxio and Object Store
   • Data is written to Alluxio storage quickly
   • Data is written to object store slowly
2. Rename temporary data to final output location
   • Rename within Alluxio is fast because it is a metadata operation
   • Rename in object store is slow because it is a copy and delete
3. Job completes to the user

When running jobs which have a large number or size of output files, the overhead of the object store dominates the run time of the workload.

Alluxio provides a way to only incur the cost of writing the data to Alluxio (fast) on the critical path. Users should configure the following Alluxio properties in the compute framework:

# Writes data only to Alluxio before returning a successful write
alluxio.user.file.writetype.default=ASYNC_THROUGH
# Does not persist the data automatically to the underlying storage, this is important because
# only the final committed data is necessary to persist
alluxio.user.file.persistence.initial.wait.time=-1
# Hints that Alluxio should treat renaming as committing data and trigger a persist operation
alluxio.user.file.persist.on.rename=true
# Determines the number of copies in Alluxio when files are not yet persisted, increase this to
# a larger number to ensure fault tolerance in case of Alluxio worker failures
alluxio.user.file.replication.durable=1
# Blacklists persisting files which contain the string "_temporary" anywhere in their path
alluxio.master.persistence.blacklist=_temporary

With this configuration, the protocol translates to the following:

1. Write temporary data to Alluxio
   • Data is written to Alluxio storage quickly
2. Rename temporary data to final output location
   • Rename within Alluxio is fast because it is a metadata operation
   • An asynchronous persist task is launched
3. Job completes to the user
4. Asynchronously write final output to object store
   • Data is written to object store slowly

Overall, a copy and delete operation in the object store is avoided, and the slow portion of writing to the object store is moved off the critical path.

In some cases, the compute framework’s commit protocol involves multiple renames or temporary files. Alluxio provides a mechanism for preventing files from being persisted by blacklisting a set of strings which are associated with temporary files. Any file which has any of the configured strings as part of its path will not be considered for persist.

For example, if

alluxio.master.persistence.blacklist=.staging,_temporary

Files such as /data/_temporary/part-00001, /data/temporary.staging will not be considered for persist. This works because eventually these temporary files will be deleted or renamed to permanent files. Because alluxio.user.file.persist.on.rename=true is set, the files will be considered for persistence again when renamed. Note that persist on rename works for directories as well as files - if a top-level directory is renamed with the persist on rename option, any files underneath the top-level directory will be considered for persistence.