Tag Archives: Distributed Computing

Distributed Computing, Java, Programming

Simple Key-Value Java Client for Riak

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Riak 2.x’s new client is powerful, but can be pretty overwhelming if all one wants is to get(), put(), and delete() key/value pairs from a distributed store. This article covers a special, but useful, case of Riak usage and the code and configuration required to achieve it. I hope it saves someone all the time it took me to work it out (thanks to engineers at Basho for their comments.)

The first thing is to establish the requirements for our store and the configuration we’ll need to achieve it. For the sake of this example, I am assuming our default bucket setup is 3 replicas (n_val=3) and will achieve consistency-by-writes (w=all). Note that you can choose any value for w, including 1. Because read-repair is triggered after a value is returned to the client, we will usually want to use a value for r that is >1 (r=2) in order to make sure we don’t get a stale value and no chance to reconcile conflicts.

Besides n_val, w, and r, there are a few more bucket and global settings we need to adjust:

  • We do not want Riak to generate “siblings”, so we need to set ‘allow_mult = false’; this lets Riak resolve conflicts for us (see next item)
  • We do want Riak to use ‘causal context’ (a combination of vector clocks and time stamps) to reconcile inconsistent values, so we set ‘last_write_wins = false’ to prevent timestamps being the only criteria
  • Because we don’t want a deletion to mask the existence of a value, we have to turn off this optimization: ‘notfound_ok = false’
  • As a result of setting ‘notfound_ok = false’, we can alleviate the resulting performance hit with ‘basic_quorum = true’; this prevents the need to wait for all replicas if a deletion is found first

The default bucket properties in riak.config would like like this:

# Default bucket properties
buckets.default.n_val =  3
buckets.default.r = 2
buckets.default.w = 3
buckets.default.allow_mult = false
buckets.default.last_write_wins = false
buckets.default.notfound_ok = false
buckets.default.basic_quorum = true

Finally, in order to prevent certain edge cases causing spooky resurrection of deleted keys, we will set the delete_mode to immediate in advanced.config

[
  {riak_kv,
    [
       %% Delete mode
        {delete_mode, immediate},
       %% Dotted version vectors 
        {dvv_enabled, true}
    ]
  }
].

The ‘dotted version vectors’ are a nice-to-have but don’t affect this article. They are simply an enhancement of the original vector clocks used by older versions of Riak.

OK, enough prolog, let’s get to the code. Here, with proper handling of causal context, updates and deletes, as well as efficiency when not actually fetching values, is a RiakKVClient:

public class RiakKVClient 
{
 
    /**
     * Constructor. Use RiakProvider.getStoreClient(name) instead.
     * @param name bucket name
     * @param client low-level Riak Java client instance
     */
    protected RiakKVClient(final String name, final com.basho.riak.client.api.RiakClient client)
    {
        this.name = name;
        this.namespace = new Namespace(name);
        this.client = client;
    }
 
    /**
     * Get a value associated with a key
     * @param key
     * @return the value associated with the given key
     * @throws IOException
     */
    public byte[] get(final String key) throws IOException {
        if (key == null)
        {
            throw new IllegalArgumentException("Key is required");
        }
 
        try {
            final FetchValue.Response response = fetchValue(key);
            if (response.isNotFound())
            {
                return null;
            }
            final RiakObject riakObject = response.getValue(RiakObject.class);
            return riakObject.getValue().getValue();
        } catch (ExecutionException e) {
            throw new IOException("Riak failed to retrieve object from bucket: " + name + " with key: " + key, e);
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
        }
        return null;
    }
 
    /**
     * Insert a value associated with a key. If a value already exists, update it.
     * @param key the key
     * @param value the value to store
     * @throws IOException
     */
    public void put(final String key, final byte[] value) throws IOException {
        if (key == null || value == null) {
            throw new IllegalArgumentException("All parameters are required");
        }
 
        try {
            // fetch in order to get the causal context
            final FetchValue.Response response = fetchMetadata(key);
            final RiakObject storeObject = new RiakObject().setValue(BinaryValue.create(value)).setContentType("binary/octet-stream");
            StoreValue.Builder builder = new StoreValue.Builder(storeObject).withLocation(new Location(namespace, key));
            final VClock vectorClock = response.getVectorClock();
            if (vectorClock != null) {
                builder = builder.withVectorClock(vectorClock);
            }
            final StoreValue storeValue = builder.build();
            client.execute(storeValue);
        } catch (ExecutionException e) {
            throw new IOException("Riak failed to store object in bucket: " + name + " with key: " + key, e);
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
        }
    }
 
    /**
     * Delete the value associated with the given key.
     * @param key the key to delete
     * @returns true if the value existed; false if o/w (and method will have no effect)
     * @throws IOException
     */
    public boolean delete(final String key) throws IOException {
        if (key == null)
        {
            throw new IllegalArgumentException("Key is required");
        }
 
        try {
            // fetch in order to get the causal context
            final FetchValue.Response response = fetchMetadata(key);
            if (response.isNotFound())
            {
                return false;
            }
            DeleteValue.Builder builder = new DeleteValue.Builder(new Location(namespace, key));
            final VClock vectorClock = response.getVectorClock();
            if (vectorClock != null) {
                builder = builder.withVClock(vectorClock);
            }
            final DeleteValue deleteValue = builder.build();
            client.execute(deleteValue);
            return !response.isNotFound() || !response.hasValues();
        } catch (ExecutionException e) {
            throw new IOException("Riak failed to store object in bucket: " + name + " with key: " + key, e);
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
        }
        return false;
    }
 
    private FetchValue.Response fetchMetadata(final String key) throws ExecutionException, InterruptedException
    {
        return fetchResponse(key, true);
    }
 
    private FetchValue.Response fetchValue(final String key) throws ExecutionException, InterruptedException
    {
        return fetchResponse(key, false);
    }
 
    private FetchValue.Response fetchResponse(final String key, boolean headOnly) throws ExecutionException, InterruptedException {
        Location loc = new Location(namespace, key);
        FetchValue.Builder builder = new FetchValue.Builder(loc);
        if (headOnly) {
            builder.withOption(FetchValue.Option.HEAD, true);
        }
        FetchValue fetch = builder.build();
        return client.execute(fetch);
 
    }
 
    private final Namespace namespace;
    private final String name;
    private final com.basho.riak.client.api.RiakClient client;
}

The only thing missing is the RiakProvider class for creating instances of RiakKVClients. Also, the code may be difficult to read given the formatting of this blog, so here’s the full Gist

If you have questions on why something is the way it is, please leave a non-anonymous comment.

References:

Understanding Riak’s Configurable Behaviors: Part 4

Computer Science, Distributed Computing, Java, Programming

A Distributed Reentrant Read-Write Lock Using a Hazelcast Data Grid

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Most Java programmers are familiar with the java.util.concurrent package and some of the handy things in it like ReentrantReadWriteLock. To recap, a ReadWriteLock solves the “Readers-Writers Problem” in computer science.

A read-write lock allows for a greater level of concurrency in accessing shared data than that permitted by a mutual exclusion lock. It exploits the fact that while only a single thread at a time (a writer thread) can modify the shared data, in many cases any number of threads can concurrently read the data (hence reader threads). In theory, the increase in concurrency permitted by the use of a read-write lock will lead to performance improvements over the use of a mutual exclusion lock. – JavaDocs, JSE 7

Additionally, a ReentrantReadWriteLock, allows any thread to acquire the same lock more than once.

This lock allows both readers and writers to reacquire read or write locks in the style of a ReentrantLock. Non-reentrant readers are not allowed until all write locks held by the writing thread have been released.

Additionally, a writer can acquire the read lock, but not vice-versa. Among other applications, reentrancy can be useful when write locks are held during calls or callbacks to methods that perform reads under read locks. If a reader tries to acquire the write lock it will never succeed.- JavaDocs, JSE 7

It would be nice to have the same semantics available to control concurrent access to resources in a distributed application, but the only implementations I’m aware of are heavy-weight (.e.g. Apache Zookeeper Shared Reentrant Read Write Lock Recipe.)

Hazelcast’s distributed in-memory data grid, on the other hand, is lightweight and easy to use. Drop a jar file into your application and off you go. Hazelcast includes distributed implementations of Maps, Lists, Queues, etc. as well as Semaphores, Locks and AtomicLongs. It seems we should be able to implement the synchronization we want using some of these distributed collections and concurrency primitives.

This blog post provides a good starting point for how to implement a ReadWrite lock using two semaphores. Though it explains the concept simply, it has two deficiencies. First, writers may starve while readers hog the lock. Second, it isn’t re-entrant. The first problem can be solved, as the blog author notes, using a third semaphore. This article (PDF) illustrates how to solve the “writers-preference” problem using a third semaphore. To make it work, though, we’ll have to replace those counters with distributed AtomicLongs.

That’s great for a non-reentrant ReadWrite lock, but what about a reentrant one? An algorithm to prevent deadlock and allow strongly reentrant usage (writers can acquire nested read locks) is explained in “Reentrant Readers-Writers” (PDF). It requires the use of a monitor–which in Java means a Lock and associated Condition–and involves keeping a per-thread count of nested locks. For the latter, I stash the count in a ThreadLocal. There are a couple of other niggly bits involving a distributed counter and a distributed boolean (which we’ll fake with an AtomicLong.)

The end result is two types of distributed locks, implemented using Hazelcast’s ISemaphore, ILock, ICondition and IAtomicLong. The only further complication is the desire to abstract the grid implementation being used (mostly useful for testing, since I know of no other grid technology that provides the data structures required here.) I use a DistributedDataStructureFactory and a DistributedLockFactory to solve those problems, as well as some helper interfaces and wrapper classes to compensate for the fact that in java.util.concurrent, Semaphore and AtomicLong are concrete classes.

Assuming you have a HazelcastInstance, usage is identical to usage of java.util.concurrent.locks.ReentrantReadWriteLock, with the exception of creation of a new lock instance.

// This can be a singleton, but additional instances aren't a problem.
DistributedLockFactory lockFactory =
    new DistributedLockFactory(new HazelcastDataStructureFactory(hazelcastInstance));
 
ReadWriteLock lock = lockFactory.getReentrantReadWriteLock("myLock");
lock.readLock().lock();
try {
    // do some stuff
}
finally {
    lock.readLock.unlock();
}

The full package, with both types of locks, helper classes, unit and integration tests has been released under the Apache 2.0 license by kind permission of my employer ThoughtWire Corporation. You can find it on GitHub here: https://github.com/ThoughtWire/hazelcast-locks

 
Update: June 13, 2014.
Shortly after releasing the first version of this package, I discovered an additional wrinkle, namely that each lock operation must deal very carefully with thread interruption so as not to leave any data structures in a state which could lead to deadlock of other threads or nodes. Specifically, all operations that call blocking methods (such as Semaphore.acquire() or Condition.await()) must catch any thrown InterruptedException and restore the lock’s original state before setting the thread’s interrupted status and returning. In practice, this is quite messy to do (!) and a worthy improvement would be to find a way to tidy it up. For the gory details on proper handling of task cancellation, see Goetz et al, “Java Concurrency in Practice, 2nd edition” (specifically, Chapter 7.)