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The Apache Cassandra Project develops a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model.
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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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 */

package org.apache.cassandra.service.reads.range;

import com.google.common.annotations.VisibleForTesting;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import org.apache.cassandra.config.DatabaseDescriptor;
import org.apache.cassandra.db.ColumnFamilyStore;
import org.apache.cassandra.db.ConsistencyLevel;
import org.apache.cassandra.db.Keyspace;
import org.apache.cassandra.db.PartitionRangeReadCommand;
import org.apache.cassandra.db.partitions.PartitionIterator;
import org.apache.cassandra.index.Index;
import org.apache.cassandra.tracing.Tracing;
import org.apache.cassandra.utils.FBUtilities;

public class RangeCommands
{
    private static final Logger logger = LoggerFactory.getLogger(RangeCommandIterator.class);

    private static final double CONCURRENT_SUBREQUESTS_MARGIN = 0.10;

    /**
     * Introduce a maximum number of sub-ranges that the coordinator can request in parallel for range queries. Previously
     * we would request up to the maximum number of ranges but this causes problems if the number of vnodes is large.
     * By default we pick 10 requests per core, assuming all replicas have the same number of cores. The idea is that we
     * don't want a burst of range requests that will back up, hurting all other queries. At the same time,
     * we want to give range queries a chance to run if resources are available.
     */
    private static final int MAX_CONCURRENT_RANGE_REQUESTS = Math.max(1, Integer.getInteger("cassandra.max_concurrent_range_requests",
                                                                                            FBUtilities.getAvailableProcessors() * 10));

    @SuppressWarnings("resource") // created iterators will be closed in CQL layer through the chain of transformations
    public static PartitionIterator partitions(PartitionRangeReadCommand command,
                                               ConsistencyLevel consistencyLevel,
                                               long queryStartNanoTime)
    {
        // Note that in general, a RangeCommandIterator will honor the command limit for each range, but will not enforce it globally.
        RangeCommandIterator rangeCommands = rangeCommandIterator(command, consistencyLevel, queryStartNanoTime);
        return command.limits().filter(command.postReconciliationProcessing(rangeCommands),
                                       command.nowInSec(),
                                       command.selectsFullPartition(),
                                       command.metadata().enforceStrictLiveness());
    }

    @VisibleForTesting
    @SuppressWarnings("resource") // created iterators will be closed in CQL layer through the chain of transformations
    static RangeCommandIterator rangeCommandIterator(PartitionRangeReadCommand command,
                                                     ConsistencyLevel consistencyLevel,
                                                     long queryStartNanoTime)
    {
        Tracing.trace("Computing ranges to query");

        Keyspace keyspace = Keyspace.open(command.metadata().keyspace);
        ReplicaPlanIterator replicaPlans = new ReplicaPlanIterator(command.dataRange().keyRange(), keyspace, consistencyLevel);

        // our estimate of how many result rows there will be per-range
        float resultsPerRange = estimateResultsPerRange(command, keyspace);
        // underestimate how many rows we will get per-range in order to increase the likelihood that we'll
        // fetch enough rows in the first round
        resultsPerRange -= resultsPerRange * CONCURRENT_SUBREQUESTS_MARGIN;
        int maxConcurrencyFactor = Math.min(replicaPlans.size(), MAX_CONCURRENT_RANGE_REQUESTS);
        int concurrencyFactor = resultsPerRange == 0.0
                                ? 1
                                : Math.max(1, Math.min(maxConcurrencyFactor, (int) Math.ceil(command.limits().count() / resultsPerRange)));
        logger.trace("Estimated result rows per range: {}; requested rows: {}, ranges.size(): {}; concurrent range requests: {}",
                     resultsPerRange, command.limits().count(), replicaPlans.size(), concurrencyFactor);
        Tracing.trace("Submitting range requests on {} ranges with a concurrency of {} ({} rows per range expected)",
                      replicaPlans.size(), concurrencyFactor, resultsPerRange);

        ReplicaPlanMerger mergedReplicaPlans = new ReplicaPlanMerger(replicaPlans, keyspace, consistencyLevel);
        return new RangeCommandIterator(mergedReplicaPlans,
                                        command,
                                        concurrencyFactor,
                                        maxConcurrencyFactor,
                                        replicaPlans.size(),
                                        queryStartNanoTime);
    }

    /**
     * Estimate the number of result rows per range in the ring based on our local data.
     * 
     * This assumes that ranges are uniformly distributed across the cluster and
     * that the queried data is also uniformly distributed.
     */
    @VisibleForTesting
    static float estimateResultsPerRange(PartitionRangeReadCommand command, Keyspace keyspace)
    {
        ColumnFamilyStore cfs = keyspace.getColumnFamilyStore(command.metadata().id);
        Index index = command.getIndex(cfs);
        float maxExpectedResults = index == null
                                   ? command.limits().estimateTotalResults(cfs)
                                   : index.getEstimatedResultRows();

        // adjust maxExpectedResults by the number of tokens this node has and the replication factor for this ks
        return (maxExpectedResults / DatabaseDescriptor.getNumTokens())
               / keyspace.getReplicationStrategy().getReplicationFactor().allReplicas;
    }
}