(b) Clone from Github or
(c) Download and unpack the latest source release from here
Note: later, when deploying Crail, make sure libdisni.so is part of your LD_LIBRARY_PATH. The easiest way to make it work is to copy libdisni.so into crail-1.0/lib
To configure Crail use crail-site.conf.template as a basis and modify it to match your environment.
cd crail-1.0/conf mv crail-site.conf.template crail-site.conf
There are a general file system properties and specific properties for the different storage tiers. A typical configuration for the general file system section may look as follows:
crail.namenode.address crail://namenode:9060 crail.storage.types org.apache.crail.storage.rdma.RdmaStorageTier crail.cachepath /dev/hugepages/cache crail.cachelimit 12884901888 crail.blocksize 1048576 crail.buffersize 1048576
In this configuration the namenode is configured to run using port 9060 on host ‘namenode’, which must be a valid host in the cluster. We further configure a single storage tier, in this case the RDMA-based DRAM tier. The cachepath property needs to point to a directory that is used by the file system to allocate memory for the client cache. Up to cachelimit size, all the memory that is used by Crail will be allocated via mmap from this location. Ideally, the directory specified in cachepath points to a hugetlbfs mountpoint. Aside from the general properties, each storage tier needs to be configured separately.
For the RDMA/DRAM tier we need to specify the interface that should be used by the storage nodes.
The datapath property specifies a path from which the storage nodes will allocate blocks of memory via mmap. Again, that path best points to a hugetlbfs mountpoint.
You want to specify how much DRAM each datanode should donate into the file system pool using the
storagelimit property. DRAM is allocated in chunks of
allocationsize, which needs to be a multiple of
crail.storage.rdma.allocationsize 1073741824 crail.storage.rdma.storagelimit 75161927680
Crail supports optimized local operations via memcpy (instead of RDMA) in case a given file operation is backed by a local storage node. The indexpath specifies where Crail will store the necessary metadata that make these optimizations possible. Important: the indexpath must NOT point to a hugetlbfs mountpoint because index files will be updated which not possible in hugetlbfs.
crail.storage.rdma.localmap true crail.storage.rdma.indexpath /index
Crail is a multi-tiered storage system. Additional tiers can be enabled by adding them to the configuration as follows.
For the NVMf storage tier we need to configure the target's IP, port and subsystem NVMe qualified name.
crail.storage.nvmf.ip 10.40.0.XX crail.storage.nvmf.port 50025 crail.storage.nvmf.nqn nqn.2017-06.io.crail:cnode
For all deployments, make sure you define CRAIL_HOME on each machine to point to the top level Crail directory.
The simplest way to run Crail is to start it manually on just a handful nodes. You will need to start the Crail namenode, plus at least one datanode. To start the namenode execute the following command on the host that is configured to be the namenode:
cd crail-1.0/ ./bin/crail namenode
To start a datanode run the following command on a host in the cluster (ideally this is a different physical machine than the one running the namenode):
Now you should have a small deployment up with just one datanode. In this case the datanode is of type RDMA/DRAM, which is the default datnode. If you want to start a different storage tier you can do so by passing a specific datanode class as follows:
./bin/crail datanode -t org.apache.crail.storage.nvmf.NvmfStorageTier
This would start the shared storage datanode. Note that configuration in crail-site.conf needs to have the specific properties set of this type of datanode, in order for this to work.
To run larger deployments start Crail using
Similarly, Crail can be stopped by using
For this to work include the list of machines to start datanodes in conf/slaves. You can start multiple datanode of different types on the same host as follows:
host02-ib host02-ib -t org.apache.crail.storage.nvmf.NvmfStorageTier host03-ib
In this example, we are configuring a Crail cluster with 2 physical hosts but 3 datanodes and two different storage tiers.
Crail provides an contains an HDFS adaptor, thus, you can interact with Crail using the HDFS shell:
Crail, however, does not implement the full HDFS shell functionality. The basic commands to copy file to/from Crail, or to move and delete files, will work.
./bin/crail fs -mkdir /test ./bin/crail fs -ls / ./bin/crail fs -copyFromLocal <path-to-local-file> /test ./bin/crail fs -cat /test/<file-name>
For the Crail shell to work properly, the HDFS configuration in crail-1.0/conf/core-site.xml needs to be configured accordingly:
<configuration> <property> <name>fs.crail.impl</name> <value>org.apache.crail.hdfs.CrailHadoopFileSystem</value> </property> <property> <name>fs.defaultFS</name> <value>crail://namenode:9060</value> </property> <property> <name>fs.AbstractFileSystem.crail.impl</name> <value>org.apache.crail.hdfs.CrailHDFS</value> </property> </configuration>
Note that the Crail HDFS interface currently cannot provide the full performance of Crail due to limitations of the HDFS API. In particular, the HDFS
FSDataOutputStream API only support heap-based
byte arrays which requires a data copy. Moreover, HDFS operations are synchronous preventing efficient pipelining of operations. Instead, applications that seek the best performance should use the Crail interface directly, as shown next.
The best way to program against Crail is to use Maven. Make sure you have the Crail dependency specified in your application pom.xml file:
<dependency> <groupId>org.apache.crail</groupId> <artifactId>crail-client</artifactId> <version>1.0</version> </dependency>
Then, create a Crail client as follows:
CrailConfiguration conf = new CrailConfiguration(); CrailStore store = CrailStore.newInstance(conf);
Make sure the crail-1.0/conf directory is part of the classpath.
Crail supports different file types. The simplest way to create a file in Crail is as follows:
CrailFile file = store.create(filename, CrailNodeType.DATAFILE, CrailStorageClass.DEFAULT, CrailLocationClass.DEFAULT).get().syncDir();
Aside from the actual filename, the ‘create()’ call takes as input the storage and location classes which are preferences for the storage tier and physical location that this file should be created in. Crail tries to satisfy these preferences later when the file is written. In the example we do not request any particular storage or location affinity.
This ‘create()’ command is non-blocking, calling ‘get()’ on the returning future object awaits the completion of the call. At that time, the file has been created, but its directory entry may not be visible. Therefore, the file may not yet show up in a file enumeration of the given parent directory. Calling ‘syncDir()’ waits to for the directory entry to be completed. Both the ‘get()’ and the ‘syncDir()’ operation can be deffered to a later time at which they may become non-blocking operations.
Once the file is created, a file stream can be obtained for writing:
CrailBufferedOutputStream outstream = file.getBufferedOutputStream(1024);
Here, we create a buffered stream so that we can pass heap byte arrays as well. We could also create a non-buffered stream using
CrailOutputStream outstream = file.getDirectOutputStream(1024);
In both cases, we pass a write hint (1024 in the example) that indicates to Crail how much data we are intending to write. This allows Crail to optimize metadatanode lookups. Crail never prefetches data, but it may fetch the metadata of the very next operation concurrently with the current data operation if the write hint allows to do so.
Once the stream has been obtained, there exist various ways to write a file. The code snippet below shows the use of the asynchronous interface:
CrailBuffer dataBuf = fs.allocateBuffer(); Future<DataResult> future = outputStream.write(dataBuf); ... future.get();
Reading files works very similar to writing. There exist various examples in org.apache.crail.tools.CrailBenchmark.
Crail is designed for user-level networking and storage. It does, however, also provide plain TCP-based storage backends for storage and RPC and, thus, can be run easily on any machine without requiring spspecial hardware support. The TCP storage backend can be enabled as follows:
The TCP RPC binding can be enabled as follows:
Crail provides a set of benchmark tools to measure the performance. Type
to get an overview of the available benchmarks. For instance, to benchmark the sequential write performance, type
./bin/crail iobench -t write -s 1048576 -k 102400 -f /tmp.dat
This will create a file of size 100G, written sequentially in a sequence of 1MB operations.
To read a file sequentially, type
./bin/crail iobench -t read -s 1048576 -k 102400 -f /tmp.dat
This command issues 102400 read operations of 1MB each.
The tool also contains benchmarks to read files randomly, or to measure the performance of opening files, etc.
Crail is used by Crail-Spark-IO, a high-performance shuffle engine for Spark. Crail-Terasort is a fast sorting benchmark for Spark based on Crail.
For any potential changes/proposals we recommend that you open a JIRA ticket to have a disucssion. After making necessary code changes, please open a pull request at Github, and update the JIRA. See here for more resources: http://crail.incubator.apache.org/community/
Please join the Crail developer mailing list for discussions and notifications. The list is at: