文章目录
- 1.概述
- 2.Distributed之表查询流程
1.概述
2.Distributed之表查询流程
Distributed表引擎不会真实存储数据，是ClickHouse提供的一个分布式查询引擎，其查询原理大致概括起来就是将server端接收到的查询请求进行重写，并发送到指定的多个server端去执行查询，最终由接到请求的server端进行汇总，最后返回给client端。这个过程可以通过源码来更清晰的了解以下。
首先，从BlockInputStreams StorageDistributed::read方法说起，因为从InterpreterSelectQuery*这类的查询都会调用BlockInputStreams 类型的read方法
BlockInputStreams StorageDistributed::read(
const Names & /*column_names*/,
const SelectQueryInfo & query_info,
const Context & context,
QueryProcessingStage::Enum processed_stage,
const size_t /*max_block_size*/,
const unsigned /*num_streams*/)
{
auto cluster = getCluster();
// 获取settings，比如内存最大使用量之类的配置
const Settings & settings = context.getSettingsRef();
// 这里就是上面提到过的重写
const auto & modified_query_ast = rewriteSelectQuery(
query_info.query, remote_database, remote_table, remote_table_function_ptr);
// 初始化一个不包含数据的Block
Block header =
InterpreterSelectQuery(query_info.query, context, SelectQueryOptions(processed_stage)).getSampleBlock();
// 根据是使用表函数还是直接使用库表的不同进入不同的逻辑
ClusterProxy::SelectStreamFactory select_stream_factory = remote_table_function_ptr
? ClusterProxy::SelectStreamFactory(
header, processed_stage, remote_table_function_ptr, context.getExternalTables())
: ClusterProxy::SelectStreamFactory(
header, processed_stage, QualifiedTableName{remote_database, remote_table}, context.getExternalTables());
// 是否自动跳过未使用的shard，如果配置了sharding_key，可以减小查询要搜索的shard范围
if (settings.optimize_skip_unused_shards)
{
if (has_sharding_key)
{
auto smaller_cluster = skipUnusedShards(cluster, query_info);
if (smaller_cluster)
{
cluster = smaller_cluster;
LOG_DEBUG(log, "Reading from " << database_name << "." << table_name << ": "
"Skipping irrelevant shards - the query will be sent to the following shards of the cluster (shard numbers): "
" " << makeFormattedListOfShards(cluster));
}
else
{
LOG_DEBUG(log, "Reading from " << database_name << "." << table_name << ": "
"Unable to figure out irrelevant shards from WHERE/PREWHERE clauses - the query will be sent to all shards of the cluster");
}
}
}
// 根据重写的ast执行查询
return ClusterProxy::executeQuery(
select_stream_factory, cluster, modified_query_ast, context, settings);
}read方法主要是sql重写及根据表函数及库表的不同逻辑初始化SelectStreamFactory，executeQuery方法是查询的入口
BlockInputStreams executeQuery(
IStreamFactory & stream_factory, const ClusterPtr & cluster,
const ASTPtr & query_ast, const Context & context, const Settings & settings)
{
BlockInputStreams res;
// 将重写的ast转为字符串，为了发送给其他server
const std::string query = queryToString(query_ast);
// 移除一些上下文的user限制，比如本次触发查询的user在其他server上，对于其他server而言
// 是个新的user，不会累积统计一些限制
Context new_context = removeUserRestrictionsFromSettings(context, settings);
// user限流设置
ThrottlerPtr user_level_throttler;
if (auto process_list_element = context.getProcessListElement())
user_level_throttler = process_list_element->getUserNetworkThrottler();
// 如果没有配置限制，那么会使用最大带宽
ThrottlerPtr throttler;
if (settings.max_network_bandwidth || settings.max_network_bytes)
{
throttler = std::make_shared(
settings.max_network_bandwidth,
settings.max_network_bytes,
"Limit for bytes to send or receive over network exceeded.",
user_level_throttler);
}
else
throttler = user_level_throttler;
// 为cluster的每个shard上创建stream_factory，并执行查询
for (const auto & shard_info : cluster->getShardsInfo())
stream_factory.createForShard(shard_info, query, query_ast, new_context, throttler, res);
return res;
}executeQuery方法主要是修改和设置一些配置，接下来是stream_factory的创建了，createForShard是个虚函数，具体实现如下
void SelectStreamFactory::createForShard(
const Cluster::ShardInfo & shard_info,
const String & query, const ASTPtr & query_ast,
const Context & context, const ThrottlerPtr & throttler,
BlockInputStreams & res)
{
// 构造一个本地流方法
auto emplace_local_stream = [&]()
{
res.emplace_back(createLocalStream(query_ast, context, processed_stage));
};
// 构造一个远程流方法
auto emplace_remote_stream = [&]()
{
auto stream = std::make_shared(shard_info.pool, query, header, context, nullptr, throttler, external_tables, processed_stage);
stream->setPoolMode(PoolMode::GET_MANY);
if (!table_func_ptr)
stream->setMainTable(main_table);
res.emplace_back(std::move(stream));
};
// 获取settings配置
const auto & settings = context.getSettingsRef();
// prefer_localhost_replica默认为true，如果shard_info还本地分片，进入以下逻辑
if (settings.prefer_localhost_replica && shard_info.isLocal())
{
StoragePtr main_table_storage;
// 根据是不是表函数方式使用不同逻辑获取main_table_storage，即一个IStorage
if (table_func_ptr)
{
const auto * table_function = table_func_ptr->as();
TableFunctionPtr table_function_ptr = TableFunctionFactory::instance().get(table_function->name, context);
main_table_storage = table_function_ptr->execute(table_func_ptr, context, table_function_ptr->getName());
}
else
main_table_storage = context.tryGetTable(main_table.database, main_table.table);
// 如果main_table_storage不存在，就尝试去其他server获取
if (!main_table_storage)
{
ProfileEvents::increment(ProfileEvents::DistributedConnectionMissingTable);
if (shard_info.hasRemoteConnections())
{
LOG_WARNING(
&Logger::get("ClusterProxy::SelectStreamFactory"),
"There is no table " << main_table.database << "." << main_table.table
<< " on local replica of shard " << shard_info.shard_num << ", will try remote replicas.");
emplace_remote_stream();
}
else
emplace_local_stream();
return;
}
const auto * replicated_storage = dynamic_cast(main_table_storage.get());
// 如果不是ReplicatedMergeTree引擎表，使用本地server，如果是就要考虑各个副本的
// 延迟情况，如果延迟不满足会在去寻找其他副本
if (!replicated_storage)
{
emplace_local_stream();
return;
}
UInt64 max_allowed_delay = settings.max_replica_delay_for_distributed_queries;
// 如果没设置最大延迟，依旧选择本地副本查询
if (!max_allowed_delay)
{
emplace_local_stream();
return;
}
UInt32 local_delay = replicated_storage->getAbsoluteDelay();
// 如果设置了最大延迟且本地延迟小于最大延迟，本地副本依然有效，选择本地副本
if (local_delay < max_allowed_delay)
{
emplace_local_stream();
return;
}
// 如果以上逻辑都没有进入，说明已经不满足延迟条件了，会执行以下代码
ProfileEvents::increment(ProfileEvents::DistributedConnectionStaleReplica);
LOG_WARNING(
&Logger::get("ClusterProxy::SelectStreamFactory"),
"Local replica of shard " << shard_info.shard_num << " is stale (delay: " << local_delay << "s.)");
// 如果没有这是fallback，就不能使用本地副本，去尝试获取远程副本
if (!settings.fallback_to_stale_replicas_for_distributed_queries)
{
if (shard_info.hasRemoteConnections())
{
emplace_remote_stream();
return;
}
else
throw Exception(
"Local replica of shard " + toString(shard_info.shard_num)
+ " is stale (delay: " + toString(local_delay) + "s.), but no other replica configured",
ErrorCodes::ALL_REPLICAS_ARE_STALE);
}
// 如果没有远程副本可选，而且设置了fallback，则才会选择本地副本
if (!shard_info.hasRemoteConnections())
{
emplace_local_stream();
return;
}
// 构造lazily_create_stream方法，避免在主线程中进行连接
auto lazily_create_stream = [
pool = shard_info.pool, shard_num = shard_info.shard_num, query, header = header, query_ast, context, throttler,
main_table = main_table, table_func_ptr = table_func_ptr, external_tables = external_tables, stage = processed_stage,
local_delay]()
-> BlockInputStreamPtr
{
auto current_settings = context.getSettingsRef();
auto timeouts = ConnectionTimeouts::getTCPTimeoutsWithFailover(
current_settings).getSaturated(
current_settings.max_execution_time);
std::vector try_results;
try
{
// 这里会去远端获取entry，getManyForTableFunction和getManyChecked方法
// 最后都会调用getManyImpl方法，只不过传入的TryGetEntryFunc不同
if (table_func_ptr)
try_results = pool->getManyForTableFunction(timeouts, ¤t_settings, PoolMode::GET_MANY);
else
try_results = pool->getManyChecked(timeouts, ¤t_settings, PoolMode::GET_MANY, main_table);
}
catch (const Exception & ex)
{
if (ex.code() == ErrorCodes::ALL_CONNECTION_TRIES_FAILED)
LOG_WARNING(
&Logger::get("ClusterProxy::SelectStreamFactory"),
"Connections to remote replicas of local shard " << shard_num << " failed, will use stale local replica");
else
throw;
}
double max_remote_delay = 0.0;
for (const auto & try_result : try_results)
{
if (!try_result.is_up_to_date)
max_remote_delay = std::max(try_result.staleness, max_remote_delay);
}
// 下面是将得到的result进行聚合
if (try_results.empty() || local_delay < max_remote_delay)
return createLocalStream(query_ast, context, stage);
else
{
std::vector connections;
connections.reserve(try_results.size());
for (auto & try_result : try_results)
connections.emplace_back(std::move(try_result.entry));
return std::make_shared(
std::move(connections), query, header, context, nullptr, throttler, external_tables, stage);
}
};
res.emplace_back(std::make_shared("LazyShardWithLocalReplica", header, lazily_create_stream));
}
else
emplace_remote_stream();
}createForShard主要是决定选择本地还是远程副本的问题，下面继续看下getManyImpl方法
std::vector ConnectionPoolWithFailover::getManyImpl(
const Settings * settings,
PoolMode pool_mode,
const TryGetEntryFunc & try_get_entry)
{
// 决定获取entries的数量
size_t min_entries = (settings && settings->skip_unavailable_shards) ? 0 : 1;
size_t max_tries = (settings ?
size_t{settings->connections_with_failover_max_tries} :
size_t{DBMS_CONNECTION_POOL_WITH_FAILOVER_DEFAULT_MAX_TRIES});
size_t max_entries;
if (pool_mode == PoolMode::GET_ALL)
{
min_entries = nested_pools.size();
max_entries = nested_pools.size();
}
else if (pool_mode == PoolMode::GET_ONE)
max_entries = 1;
else if (pool_mode == PoolMode::GET_MANY)
max_entries = settings ? size_t(settings->max_parallel_replicas) : 1;
else
throw DB::Exception("Unknown pool allocation mode", DB::ErrorCodes::LOGICAL_ERROR);
// 获取策略，NEAREST_HOSTNAME、IN_ORDER、RANDOM、FIRST_OR_RANDOM
GetPriorityFunc get_priority;
switch (settings ? LoadBalancing(settings->load_balancing) : default_load_balancing)
{
case LoadBalancing::NEAREST_HOSTNAME:
get_priority = [&](size_t i) { return hostname_differences[i]; };
break;
case LoadBalancing::IN_ORDER:
get_priority = [](size_t i) { return i; };
break;
case LoadBalancing::RANDOM:
break;
case LoadBalancing::FIRST_OR_RANDOM:
get_priority = [](size_t i) -> size_t { return i >= 1; };
break;
}
bool fallback_to_stale_replicas = settings ? bool(settings->fallback_to_stale_replicas_for_distributed_queries) : true;
return Base::getMany(min_entries, max_entries, max_tries, try_get_entry, get_priority, fallback_to_stale_replicas);
}getManyImpl方法主要是决定用多少entries以及远程副本的策略，继续看getMany方法
PoolWithFailoverBase::getMany(
size_t min_entries, size_t max_entries, size_t max_tries,
const TryGetEntryFunc & try_get_entry,
const GetPriorityFunc & get_priority,
bool fallback_to_stale_replicas)
{
......
std::string fail_messages;
bool finished = false;
while (!finished)
{
for (size_t i = 0; i < shuffled_pools.size(); ++i)
{
if (up_to_date_count >= max_entries
|| entries_count + failed_pools_count >= nested_pools.size())
{
finished = true;
break;
}
ShuffledPool & shuffled_pool = shuffled_pools[i];
TryResult & result = try_results[i];
if (shuffled_pool.error_count >= max_tries || !result.entry.isNull())
continue;
std::string fail_message;
// 这里就是调用了上面提到的TryGetEntryFunc方法来真正的获取entry
result = try_get_entry(*shuffled_pool.pool, fail_message);
if (!fail_message.empty())
fail_messages += fail_message + '\n';
if (!result.entry.isNull())
{
++entries_count;
if (result.is_usable)
{
++usable_count;
if (result.is_up_to_date)
++up_to_date_count;
}
}
else
{
LOG_WARNING(log, "Connection failed at try №"
<< (shuffled_pool.error_count + 1) << ", reason: " << fail_message);
ProfileEvents::increment(ProfileEvents::DistributedConnectionFailTry);
shuffled_pool.error_count = std::min(max_error_cap, shuffled_pool.error_count + 1);
if (shuffled_pool.error_count >= max_tries)
{
++failed_pools_count;
ProfileEvents::increment(ProfileEvents::DistributedConnectionFailAtAll);
}
}
}
}
if (usable_count < min_entries)
throw DB::NetException(
"All connection tries failed. Log: \n\n" + fail_messages + "\n",
DB::ErrorCodes::ALL_CONNECTION_TRIES_FAILED);
try_results.erase(
std::remove_if(
try_results.begin(), try_results.end(),
[](const TryResult & r) { return r.entry.isNull() || !r.is_usable; }),
try_results.end());
// 以下代码主要是对结果进行排序
std::stable_sort(
try_results.begin(), try_results.end(),
[](const TryResult & left, const TryResult & right)
{
return std::forward_as_tuple(!left.is_up_to_date, left.staleness)
< std::forward_as_tuple(!right.is_up_to_date, right.staleness);
});
......
return try_results;
}getMany方法就是真正获取entry并进行排序的过程，至此，Distributed表的查询的大体流程就完整了。
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