wlan-cloud-lib-cppkafka/include/cppkafka/utils/roundrobin_poll_strategy.h

/*
 * Copyright (c) 2017, Matias Fontanini
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are
 * met:
 *
 * * Redistributions of source code must retain the above copyright
 *   notice, this list of conditions and the following disclaimer.
 * * Redistributions in binary form must reproduce the above
 *   copyright notice, this list of conditions and the following disclaimer
 *   in the documentation and/or other materials provided with the
 *   distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
 * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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#ifndef CPPKAFKA_ROUNDROBIN_POLL_STRATEGY_H
#define CPPKAFKA_ROUNDROBIN_POLL_STRATEGY_H

#include <map>
#include <string>
#include "../exceptions.h"
#include "../consumer.h"
#include "../queue.h"
#include "poll_strategy_base.h"

namespace cppkafka {

/**
 * \brief This adapter changes the default polling strategy of the Consumer into a fair round-robin
 *        polling mechanism.
 *
 * The default librdkafka (and cppkafka) poll() and poll_batch() behavior is to consume batches of
 * messages from each partition in turn. For performance reasons, librdkafka pre-fetches batches
 * of messages from the kafka broker (one batch from each partition), and stores them locally in
 * partition queues. Since all the internal partition queues are forwarded by default unto the
 * group consumer queue (one per consumer), these batches end up being polled and consumed in the
 * same sequence order.
 * This adapter allows fair round-robin polling of all assigned partitions, one message at a time
 * (or one batch at a time if poll_batch() is used). Note that poll_batch() has nothing to do with
 * the internal batching mechanism of librdkafka.
 *
 * Example code on how to use this:
 *
 * \code
 * // Create a consumer
 * Consumer consumer(...);
 * consumer.subscribe({ "my_topic" });
 *
 * // Optionally set the callbacks. This must be done *BEFORE* creating the strategy adapter
 * consumer.set_assignment_callback(...);
 * consumer.set_revocation_callback(...);
 * consumer.set_rebalance_error_callback(...);
 *
 * // Create the adapter and use it for polling
 * RoundRobinPollStrategy poll_strategy(consumer);
 *
 * while (true) {
 *     // Poll each partition in turn
 *     Message msg = poll_strategy.poll();
 *     if (msg) {
 *         // process valid message
 *         }
 *     }
 * }
 * \endcode
 *
 * \warning Calling directly poll() or poll_batch() on the Consumer object while using this adapter will
 * lead to undesired results since the RoundRobinPollStrategy modifies the internal queuing mechanism of
 * the Consumer instance it owns.
 */

class RoundRobinPollStrategy : public PollStrategyBase {
public:
    RoundRobinPollStrategy(Consumer& consumer);
    
    ~RoundRobinPollStrategy();
    
    /**
     * \sa PollInterface::poll
     */
    Message poll() override;
    
    /**
     * \sa PollInterface::poll
     */
    Message poll(std::chrono::milliseconds timeout) override;

    /**
     * \sa PollInterface::poll_batch
     */
    template <typename Allocator>
    std::vector<Message, Allocator> poll_batch(size_t max_batch_size,
                                               const Allocator& alloc);
    std::vector<Message> poll_batch(size_t max_batch_size) override;

    /**
     * \sa PollInterface::poll_batch
     */
    template <typename Allocator>
    std::vector<Message, Allocator> poll_batch(size_t max_batch_size,
                                               std::chrono::milliseconds timeout,
                                               const Allocator& alloc);
    std::vector<Message> poll_batch(size_t max_batch_size,
                                    std::chrono::milliseconds timeout) override;
    
protected:
    /**
     * \sa PollStrategyBase::reset_state
     */
    void reset_state() final;
    
    QueueData& get_next_queue();
    
private:
    template <typename Allocator>
    void consume_batch(Queue& queue,
                       std::vector<Message, Allocator>& messages,
                       ssize_t& count,
                       std::chrono::milliseconds timeout,
                       const Allocator& alloc);
    
    void restore_forwarding();
    
    // Members
    QueueMap::iterator  queue_iter_;
};

// Implementations
template <typename Allocator>
std::vector<Message, Allocator> RoundRobinPollStrategy::poll_batch(size_t max_batch_size,
                                                                   const Allocator& alloc) {
    return poll_batch(max_batch_size, get_consumer().get_timeout(), alloc);
}

template <typename Allocator>
std::vector<Message, Allocator> RoundRobinPollStrategy::poll_batch(size_t max_batch_size,
                                                                   std::chrono::milliseconds timeout,
                                                                   const Allocator& alloc) {
    std::vector<Message, Allocator> messages(alloc);
    ssize_t count = max_batch_size;
    
    // batch from the group event queue first (non-blocking)
    consume_batch(get_consumer_queue().queue, messages, count, std::chrono::milliseconds(0), alloc);
    size_t num_queues = get_partition_queues().size();
    while ((count > 0) && (num_queues--)) {
        // batch from the next partition (non-blocking)
        consume_batch(get_next_queue().queue, messages, count, std::chrono::milliseconds(0), alloc);
    }
    // we still have space left in the buffer
    if (count > 0) {
        // wait on the event queue until timeout
        consume_batch(get_consumer_queue().queue, messages, count, timeout, alloc);
    }
    return messages;
}

template <typename Allocator>
void RoundRobinPollStrategy::consume_batch(Queue& queue,
                                           std::vector<Message, Allocator>& messages,
                                           ssize_t& count,
                                           std::chrono::milliseconds timeout,
                                           const Allocator& alloc) {
    std::vector<Message, Allocator> queue_messages = queue.consume_batch(count, timeout, alloc);
    if (queue_messages.empty()) {
        return;
    }
    // concatenate both lists
    messages.insert(messages.end(),
                    make_move_iterator(queue_messages.begin()),
                    make_move_iterator(queue_messages.end()));
    // reduce total batch count
    count -= queue_messages.size();
}

} //cppkafka

#endif //CPPKAFKA_ROUNDROBIN_POLL_STRATEGY_H