I have Kafka engine table, MV and destination MT table.
Kafka table create query:
```
CREATE TABLE abc.Kafka_TestSource (...) 
ENGINE = Kafka 
SETTINGS kafka_broker_list = '...'
, format_avro_schema_registry_url = '...'
, kafka_topic_list = '...'
, kafka_group_name = 'Test_KafkaToCH'
, kafka_format = 'AvroConfluent'
, kafka_num_consumers = 1
, kafka_thread_per_consumer = 0
```

Due to Grafana consumer group stats dashboard it reads the topic with average speed ~11k records per second.
I recreate Kafka table with `kafka_num_consumers = 9` (kafka topic has 9 partitions) and it still reads with the same speed ~11k rps, **system.metrics** also shows that only 1 pool is being used:
```
SELECT
    m.metric,
    m.value
FROM system.metrics AS m
WHERE m.metric LIKE 'Background%'

Query id: 831a9285-e91b-45a4-8a1b-6b536d6860fe

┌─metric──────────────────────────────────┬─value─┐
│ BackgroundMergesAndMutationsPoolTask    │     4 │
│ BackgroundFetchesPoolTask               │     0 │
│ BackgroundCommonPoolTask                │     0 │
│ BackgroundMovePoolTask                  │     0 │
│ BackgroundSchedulePoolTask              │     0 │
│ BackgroundBufferFlushSchedulePoolTask   │     0 │
│ BackgroundDistributedSchedulePoolTask   │     0 │
│ BackgroundMessageBrokerSchedulePoolTask │     1 │
└─────────────────────────────────────────┴───────┘
```

If I set `kafka_thread_per_consumer = 1` then **BackgroundMessageBrokerSchedulePoolTask** in **system.metrics** raises up to 9 and read speed in Grafana raises up to 100 records per second:
![pic](https://github.com/sokolov-alexey/pics/blob/main/ch_kafka.PNG?raw=true)
1. `kafka_num_consumers = 1, kafka_thread_per_consumer = 0`
2. `kafka_num_consumers = 9, kafka_thread_per_consumer = 0`
3. `kafka_num_consumers = 9, kafka_thread_per_consumer = 1`

Why `kafka_num_consumers` doesn't work without `kafka_thread_per_consumer`?

ClickHouse 21.11.4.14.
{
        auto source = std::make_shared<KafkaSource>(*this, storage_snapshot, kafka_context, block_io.pipeline.getHeader().getNames(), log, block_size, false);
        sources.emplace_back(source);
        pipes.emplace_back(source);
        
        StreamLocalLimits limits;
        limits.speed_limits.max_execution_time = kafka_settings->kafka_flush_interval_ms.changed
                                                 ? kafka_settings->kafka_flush_interval_ms
                                                 : getContext()->getSettingsRef().stream_flush_interval_ms;
        limits.timeout_overflow_mode = OverflowMode::BREAK;
        source->setLimits(limits);
    }
    auto pipe = Pipe::unitePipes(std::move(pipes));
    
    
    size_t rows = 0;
    {
        block_io.pipeline.complete(std::move(pipe));
        block_io.pipeline.setProgressCallback([&](const Progress & progress) { rows += progress.read_rows.load(); });
        CompletedPipelineExecutor executor(block_io.pipeline);
        executor.execute();
    }
    bool some_stream_is_stalled = false;
    for (auto & source : sources)
    {
        some_stream_is_stalled = some_stream_is_stalled || source->isStalled();
        source->commit();
    }
    UInt64 milliseconds = watch.elapsedMilliseconds();
    LOG_DEBUG(log, "Pushing {} rows to {} took {} ms.",