线程锁内的多线程

Question

I am working on speeding up the execution of some processes that publish a bulk set of records (Mostly in the millions) to Elasticsearch. In my C# code I have already implemented a multi-threaded solution using Dataflow as scaffolded below:

var fetchRecords = new TransformBlock<?, ?>(() => { ... });
var sendRecordsToElastic = new ActionBlock<List<?>>(records => sendBulkRequest(records));

fetchRecords.LinkTo(sendRecordsToElastic, { PropogateCompletion = true });

fetchRecords.Post("Start");

And then the send bulk request call I want to implement:

public IBulkResponse sendBulkRequest(List<?> records)
{
    lock(SomeStaticObject)
    {
       // Execute several new threads to send records in bulk
    }
}

My question for you is on the practicality for executing additional threads within a lock that exists as part of a Dataflow pipeline.

Is this ok? Could I see any potential hiccups in performance, execution, cache/memory misses, etc?

Any insight would be gladly accepted.

Answer 1

You may want to use BulkAll here, which implements the observable pattern to make concurrent bulk requests to Elasticsearch. Here's an example

void Main()
{   
    var pool = new SingleNodeConnectionPool(new Uri("http://localhost:9200"));
    var connectionSettings = new ConnectionSettings(pool);

    var client = new ElasticClient(connectionSettings);
    var indexName = "bulk-index";

    if (client.IndexExists(indexName).Exists)
        client.DeleteIndex(indexName);

    client.CreateIndex(indexName, c => c
        .Settings(s => s
            .NumberOfShards(3)
            .NumberOfReplicas(0)
        )
        .Mappings(m => m
            .Map<DeviceStatus>(p => p.AutoMap())
        )
    );

    var size = 500;

    // set up the observable
    var bulkAllObservable = client.BulkAll(GetDeviceStatus(), b => b
        .Index(indexName)
        .MaxDegreeOfParallelism(4)
        .RefreshOnCompleted()
        .Size(size)
    );

    var countdownEvent = new CountdownEvent(1);

    Exception exception = null;

    // set up an observer. Delegates passed are:
    // 1. onNext
    // 2. onError
    // 3. onCompleted
    var bulkAllObserver = new BulkAllObserver(
        response => Console.WriteLine($"Indexed {response.Page * size} with {response.Retries} retries"),
        ex => 
        {
            // capture exception for throwing outside Observer.
            // You may decide to do something different here
            exception = ex;
            countdownEvent.Signal();
        },
        () => 
        {
            Console.WriteLine("Finished");
            countdownEvent.Signal();
        });

    // subscribe to the observable          
    bulkAllObservable.Subscribe(bulkAllObserver);

    // wait indefinitely for it to finish. May want to put a
    // max timeout on this  
    countdownEvent.Wait();

    if (exception != null) 
    {
        throw exception;
    }
}

// lazily enumerated collection
private static IEnumerable<DeviceStatus> GetDeviceStatus()
{
    for (var i = 0; i < DocumentCount; i++)
        yield return new DeviceStatus(i); 
}

private const int DocumentCount = 20000;

public class DeviceStatus
{
    public DeviceStatus(int id) => Id = id;
    public int Id {get;set;}
}

If you don't need to do anything special in the observer, you can use the .Wait() method on the observable

var bulkAllObservable = client.BulkAll(GetDeviceStatus(), b => b
    .Index(indexName)
    .MaxDegreeOfParallelism(4)
    .RefreshOnCompleted()
    .Size(size)
)
.Wait(
    TimeSpan.FromHours(1), 
    response => Console.WriteLine($"Indexed {response.Page * size} with {response.Retries} retries")
);

There are observable methods for BulkAll , ScrollAll and Reindex (although there is ReindexOnServer which reindexes within Elasticsearch and maps to the Reindex API - the Reindex method predates this)