HDFS维持副本平衡的流程

HDFS上文件的block默认会存放3个副本,一个副本存在一个rack的某个dn上,第二个和第三个副本存放在另一个机架的某两个dn上,nn会维持block的副本平衡,但是当集群上的副本数超过3个时,nn会删除那些节点上的副本来维护副本平衡呢?当集群上的副本数少于3个时,会原则那些节点作为原点进行复制呢?那就查下代码看nn是怎么搞的

HDFS删除多余副本

多余副本的场景

  • rack0上有一个副本,rack1上有两个副本,此时rack0的dn下线,nn维持副本平衡,复制一个副本到其它的rack上,3副本平衡,但是下线的dn又重新上线了,这就出现了4个副本,需要删除一个副本
  • 调用hadoop fs -setrep -R 3相关命令人为修改副本的个数,

删除多余副本

BlockManager主要管理block存储在集群中的相关信息,查看其processOverReplicatedBlock方法,处理多余副本,

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/**
* 查出有多少个副本
* 如果有多余的副本,则调用chooseExcessReplicates()
* 将多余副本放入excessReplicateMap中
*/
private void processOverReplicatedBlock(final Block block,
final short replication, final DatanodeDescriptor addedNode,
DatanodeDescriptor delNodeHint) {
...
// block正常副本所在节点的集合
Collection<DatanodeStorageInfo> nonExcess = new ArrayList<DatanodeStorageInfo>();
// 用于存放该block损坏的副本所在的节点(多余的副本不包含损坏的副本)
Collection<DatanodeDescriptor> corruptNodes = corruptReplicas
.getNodes(block);
for(DatanodeStorageInfo storage : blocksMap.getStorages(block, State.NORMAL)) {
final DatanodeDescriptor cur = storage.getDatanodeDescriptor();
...
LightWeightLinkedSet<Block> excessBlocks = excessReplicateMap.get(cur
.getDatanodeUuid());
if (excessBlocks == null || !excessBlocks.contains(block)) {
if (!cur.isDecommissionInProgress() && !cur.isDecommissioned()) {
// exclude corrupt replicas
if (corruptNodes == null || !corruptNodes.contains(cur)) {
nonExcess.add(storage);
}
}
}
}
//
chooseExcessReplicates(nonExcess, block, replication,
addedNode, delNodeHint, blockplacement);
}

processOverReplicatedBlock将block的副本所在的节点放在nonExcess集合中,然后调用chooseExcessReplicates

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/**
* We want "replication" replicates for the block, but we now have too many.
* In this method, copy enough nodes from 'srcNodes' into 'dstNodes' such that:
*
* srcNodes.size() - dstNodes.size() == replication
*
* 尽量使副本遍布rack,尽量选择剩余空间不足的节点
* 选取规则是首先从不只一个副本的机架上选取剩余空间最小的节点
* So removing such a replica won't remove a rack. ??????什么意思
* 如果没有这样的节点可以选择那就选剩余空间最小的节点
*/
private void chooseExcessReplicates(final Collection<DatanodeStorageInfo> nonExcess,
Block b, short replication,
DatanodeDescriptor addedNode,
DatanodeDescriptor delNodeHint,
BlockPlacementPolicy replicator) {
assert namesystem.hasWriteLock();
// first form a rack to datanodes map and
// BlockCollection是该block的所属信息,INodeFile实现此接口
BlockCollection bc = getBlockCollection(b);
final BlockStoragePolicy storagePolicy = storagePolicySuite.getPolicy(bc.getStoragePolicyID());
// 这是个什么东西待验证????
final List<StorageType> excessTypes = storagePolicy.chooseExcess(
replication, DatanodeStorageInfo.toStorageTypes(nonExcess));
// rack和dn的映射
final Map<String, List<DatanodeStorageInfo>> rackMap
= new HashMap<String, List<DatanodeStorageInfo>>();
// 某个机架上副本数超过1个的dn集合
final List<DatanodeStorageInfo> moreThanOne = new ArrayList<DatanodeStorageInfo>();
// 机架上只有一个副本的dn集合
final List<DatanodeStorageInfo> exactlyOne = new ArrayList<DatanodeStorageInfo>();
// split nodes into two sets
// moreThanOne contains nodes on rack with more than one replica
// exactlyOne contains the remaining nodes
// 将nonExcess根据所在机架上副本的个数分为两个集合
replicator.splitNodesWithRack(nonExcess, rackMap, moreThanOne, exactlyOne);
// pick one node to delete that favors the delete hint
// otherwise pick one with least space from priSet if it is not empty
// otherwise one node with least space from remains
boolean firstOne = true;
final DatanodeStorageInfo delNodeHintStorage
= DatanodeStorageInfo.getDatanodeStorageInfo(nonExcess, delNodeHint);
final DatanodeStorageInfo addedNodeStorage
= DatanodeStorageInfo.getDatanodeStorageInfo(nonExcess, addedNode);
// 删除多余副本,replication为期望副本数
while (nonExcess.size() - replication > 0) {
final DatanodeStorageInfo cur;
// delNodeHint不为null,则先从delNodeHint中删除
// 只有第一次才会判断
if (useDelHint(firstOne, delNodeHintStorage, addedNodeStorage,
moreThanOne, excessTypes)) {
cur = delNodeHintStorage;
} else { // regular excessive replica removal
cur = replicator.chooseReplicaToDelete(bc, b, replication,
moreThanOne, exactlyOne, excessTypes);
}
firstOne = false;
// adjust rackmap, moreThanOne, and exactlyOne
replicator.adjustSetsWithChosenReplica(rackMap, moreThanOne,
exactlyOne, cur);
nonExcess.remove(cur);
addToExcessReplicate(cur.getDatanodeDescriptor(), b);
//
// The 'excessblocks' tracks blocks until we get confirmation
// that the datanode has deleted them; the only way we remove them
// is when we get a "removeBlock" message.
//
// The 'invalidate' list is used to inform the datanode the block
// should be deleted. Items are removed from the invalidate list
// upon giving instructions to the namenode.
//
addToInvalidates(b, cur.getDatanodeDescriptor());
blockLog.info("BLOCK* chooseExcessReplicates: "
+"("+cur+", "+b+") is added to invalidated blocks set");
}
}

chooseExcessReplicates的处理逻辑是,调用replicator.splitNodesWithRack将nonExcess分为两个集合,然后循环的调用replicator.chooseReplicaToDelete选出要删除副本的节点,放入excessReplicateMap和invalidateBlocks中,等待delete掉。

moreThanOne和exactlyOne的划分规则在splitNodesWithRack中,代码如下:

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public void splitNodesWithRack(
final Iterable<DatanodeStorageInfo> storages,
final Map<String, List<DatanodeStorageInfo>> rackMap,
final List<DatanodeStorageInfo> moreThanOne,
final List<DatanodeStorageInfo> exactlyOne) {
// 构建rackMap
// rackMap中key是rack name,value是副本的list
for(DatanodeStorageInfo s: storages) {
final String rackName = getRack(s.getDatanodeDescriptor());
List<DatanodeStorageInfo> storageList = rackMap.get(rackName);
if (storageList == null) {
storageList = new ArrayList<DatanodeStorageInfo>();
rackMap.put(rackName, storageList);
}
storageList.add(s);
}
// split nodes into two sets
// 根据value的个数进行划分exactlyOne和moreThanOne
for(List<DatanodeStorageInfo> storageList : rackMap.values()) {
if (storageList.size() == 1) {
// exactlyOne contains nodes on rack with only one replica
exactlyOne.add(storageList.get(0));
} else {
// moreThanOne contains nodes on rack with more than one replica
moreThanOne.addAll(storageList);
}
}
}

splitNodesWithRack划分结束之后,在chooseExcessReplicates中进行while循环来删除多余的副本,直到达到期望副本个数。

删除节点的选择是在chooseReplicaToDelete中决定的,该方法在BlockPlacementPolicyDefault中被重写。选取规则为

  • 如果moreThanOne不是empty,则先把moreThanOne做为节点集合
  • 首先选择心跳时间间隔最长的节点或者
  • 如果所有的心跳都在允许的间隔之内,则选择剩余空间最少的节点,
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// BlockPlacementPolicyDefault.class
public DatanodeStorageInfo chooseReplicaToDelete(BlockCollection bc,
Block block, short replicationFactor,
Collection<DatanodeStorageInfo> first,
Collection<DatanodeStorageInfo> second,
final List<StorageType> excessTypes) {
// 两次心跳之间允许的最大间隔,为时间点数值
long oldestHeartbeat =
now() - heartbeatInterval * tolerateHeartbeatMultiplier;
DatanodeStorageInfo oldestHeartbeatStorage = null;
long minSpace = Long.MAX_VALUE;
DatanodeStorageInfo minSpaceStorage = null;
// 如果first集合不为null,则从first集合中选取要删除的节点,否则从second
for(DatanodeStorageInfo storage : pickupReplicaSet(first, second)) {
if (!excessTypes.contains(storage.getStorageType())) {
continue;
}
final DatanodeDescriptor node = storage.getDatanodeDescriptor();
long free = node.getRemaining();
long lastHeartbeat = node.getLastUpdate();
// 心跳间隔最长的节点赋值
if(lastHeartbeat < oldestHeartbeat) {
oldestHeartbeat = lastHeartbeat;
oldestHeartbeatStorage = storage;
}
// 对剩余空间最小的节点赋值
if (minSpace > free) {
minSpace = free;
minSpaceStorage = storage;
}
}
// 首先选择心跳时间间隔最长的节点或者
// 如果所有的心跳都在允许的间隔之内,则选择剩余空间最少的节点,
final DatanodeStorageInfo storage;
if (oldestHeartbeatStorage != null) {
storage = oldestHeartbeatStorage;
} else if (minSpaceStorage != null) {
storage = minSpaceStorage;
} else {
return null;
}
excessTypes.remove(storage.getStorageType());
return storage;
}

选出要删除的节点cur之后,调用adjustSetsWithChosenReplica重新调整rackMap、moreThanOne和exactlyOne,并调用addToExcessReplicate将cur放入excessReplicateMap

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private void addToExcessReplicate(DatanodeInfo dn, Block block) {
assert namesystem.hasWriteLock();
LightWeightLinkedSet<Block> excessBlocks = excessReplicateMap.get(dn.getDatanodeUuid());
if (excessBlocks == null) {
excessBlocks = new LightWeightLinkedSet<Block>();
excessReplicateMap.put(dn.getDatanodeUuid(), excessBlocks);
}
if (excessBlocks.add(block)) {
excessBlocksCount.incrementAndGet();
if(blockLog.isDebugEnabled()) {
blockLog.debug("BLOCK* addToExcessReplicate:"
+ " (" + dn + ", " + block
+ ") is added to excessReplicateMap");
}
}
}

最后会将cur放入invalidateBlocks中,随后cur会被删除

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void addToInvalidates(final Block block, final DatanodeInfo datanode) {
if (!namesystem.isPopulatingReplQueues()) {
return;
}
invalidateBlocks.add(block, datanode, true);
}

invalidateBlocks在哪被删除(在ReplicationMonitor的中)

HDFS修复缺少副本

当HDFS上某个block的副本数低于期望的副本数时,会调用processMisReplicatedBlock进行处理

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/**
* Process a single possibly misreplicated block. This adds it to the
* appropriate queues if necessary, and returns a result code indicating
* what happened with it.
*/
private MisReplicationResult processMisReplicatedBlock(BlockInfo block) {
BlockCollection bc = block.getBlockCollection();
// bc 为null,则该block没有所属文件信息,是无效的,应该被删除
if (bc == null) {
// block does not belong to any file
addToInvalidates(block);
return MisReplicationResult.INVALID;
}
if (!block.isComplete()) {
// Incomplete blocks are never considered mis-replicated --
// they'll be reached when they are completed or recovered.
return MisReplicationResult.UNDER_CONSTRUCTION;
}
// calculate current replication
short expectedReplication = bc.getBlockReplication();
// 计算该block的副本数,不包含损坏的
NumberReplicas num = countNodes(block);
int numCurrentReplica = num.liveReplicas();
// add to under-replicated queue if need to be
if (isNeededReplication(block, expectedReplication, numCurrentReplica)) {
if (neededReplications.add(block, numCurrentReplica, num
.decommissionedReplicas(), expectedReplication)) {
return MisReplicationResult.UNDER_REPLICATED;
}
}
if (numCurrentReplica > expectedReplication) {
if (num.replicasOnStaleNodes() > 0) {
// If any of the replicas of this block are on nodes that are
// considered "stale", then these replicas may in fact have
// already been deleted. So, we cannot safely act on the
// over-replication until a later point in time, when
// the "stale" nodes have block reported.
return MisReplicationResult.POSTPONE;
}
// over-replicated block
// 超出了expectedReplication,则删除多余副本
processOverReplicatedBlock(block, expectedReplication, null, null);
return MisReplicationResult.OVER_REPLICATED;
}
return MisReplicationResult.OK;
}

processMisReplicatedBlock根据该block的信息,将block打上标签并放入不同的队列中进行处理。利用countNodes计算其副本数

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/**
* Return the number of nodes hosting a given block, grouped
* by the state of those replicas.
*/
public NumberReplicas countNodes(Block b) {
int decommissioned = 0;
int live = 0;
int corrupt = 0;
int excess = 0;
int stale = 0;
Collection<DatanodeDescriptor> nodesCorrupt = corruptReplicas.getNodes(b);
for(DatanodeStorageInfo storage : blocksMap.getStorages(b, State.NORMAL)) {
final DatanodeDescriptor node = storage.getDatanodeDescriptor();
if ((nodesCorrupt != null) && (nodesCorrupt.contains(node))) {
corrupt++;
} else if (node.isDecommissionInProgress() || node.isDecommissioned()) {
decommissioned++;
} else {
LightWeightLinkedSet<Block> blocksExcess = excessReplicateMap.get(node
.getDatanodeUuid());
if (blocksExcess != null && blocksExcess.contains(b)) {
excess++;
} else {
live++;
}
}
if (storage.areBlockContentsStale()) {
stale++;
}
}
return new NumberReplicas(live, decommissioned, corrupt, excess, stale);
}

processMisReplicatedBlock中用isNeededReplication判断是否需要进行增加副本数,current < expected || !blockHasEnoughRacks(b)为true时,则调用neededReplications.add进行增加副本,neededReplications是UnderReplicatedBlocks的实例,UnderReplicatedBlocks是HDFS中关于块复制的一个重要数据结构。add方法如下:

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synchronized boolean add(Block block,
int curReplicas,
int decomissionedReplicas,
int expectedReplicas) {
assert curReplicas >= 0 : "Negative replicas!";
int priLevel = getPriority(block, curReplicas, decomissionedReplicas,
expectedReplicas);
if(priLevel != LEVEL && priorityQueues.get(priLevel).add(block)) {
if(NameNode.blockStateChangeLog.isDebugEnabled()) {
NameNode.blockStateChangeLog.debug(
"BLOCK* NameSystem.UnderReplicationBlock.add:"
+ block
+ " has only " + curReplicas
+ " replicas and need " + expectedReplicas
+ " replicas so is added to neededReplications"
+ " at priority level " + priLevel);
}
return true;
}
return false;
}

add将根据block相关副本的优先级放入under replication queue中,优先级从getPriority中获取,级别有5中,从0开始,0的优先级最高,则4最低。

  1. QUEUE_HIGHEST_PRIORITY = 0
    最高优先级,主要针对数据块副本数非常的、严重的不足的情况,当前副本数低于期望值,且仅有1个或者干脆没有,比如副本数仅有1个,或者副本数干脆为0,但是还存在退役副本,这种情况最危险,数据最容易丢失,所以复制的优先级也最高
  2. QUEUE_VERY_UNDER_REPLICATED = 1
    主要针对数据块副本数不足但没有上面严重的情况,如当前副本数低于期望值,但是副本数大于1其判断公式为当前副本数curReplicas乘以3还小于期望副本数expectedReplicas,这种情况也比较危险,数据也容易丢失,所以复制的优先级也很高
  3. QUEUE_UNDER_REPLICATED = 2
    主要针对数据块副本数低于期望值,但是还不是很严重,也可以理解为正常缺失副本块
  4. QUEUE_REPLICAS_BADLY_DISTRIBUTED = 3
    有足够(大于或者等于正确的副本数)的副本个数,但是并不符合副本的放置策略,副本分布不均衡
  5. QUEUE_WITH_CORRUPT_BLOCKS = 4
    主要针对损坏的数据块的情况,其副本数位0,但是还没有退役副本
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private int getPriority(Block block,
int curReplicas,
int decommissionedReplicas,
int expectedReplicas) {
assert curReplicas >= 0 : "Negative replicas!";
if (curReplicas >= expectedReplicas) {
// Block has enough copies, but not enough racks
return QUEUE_REPLICAS_BADLY_DISTRIBUTED;
} else if (curReplicas == 0) {
// If there are zero non-decommissioned replicas but there are
// some decommissioned replicas, then assign them highest priority
if (decommissionedReplicas > 0) {
return QUEUE_HIGHEST_PRIORITY;
}
//all we have are corrupt blocks
return QUEUE_WITH_CORRUPT_BLOCKS;
} else if (curReplicas == 1) {
//only on replica -risk of loss
// highest priority
return QUEUE_HIGHEST_PRIORITY;
} else if ((curReplicas * 3) < expectedReplicas) {
//there is less than a third as many blocks as requested;
//this is considered very under-replicated
return QUEUE_VERY_UNDER_REPLICATED;
} else {
//add to the normal queue for under replicated blocks
return QUEUE_UNDER_REPLICATED;
}
}

通过getPriority得到优先级之后,从priorityQueues list中拿到相同优先级的LightWeightLinkedSet set,将block放入set中。最后在processMisReplicatedBlock方法中返回该block的标记MisReplicationResult.UNDER_REPLICATED

通过上面的代码nn将缺少副本的block根据复制优先级放入不同的queue中,等待复制线程进行复制。

附加:副本默认放置策略

默认的放置策略大家都熟悉,3副本分布在两个rack上,一个rack上有一个副本,另一个rack上有两个副本,这里就只简单的列出代码的部分实现,代码入口是BlockPlacementPolicyDefault.chooseTarget,这里只列主要的逻辑代码chooseTarget:

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private Node chooseTarget(int numOfReplicas,
Node writer,
final Set<Node> excludedNodes,
final long blocksize,
final int maxNodesPerRack,
final List<DatanodeStorageInfo> results,
final boolean avoidStaleNodes,
final BlockStoragePolicy storagePolicy,
final EnumSet<StorageType> unavailableStorages,
final boolean newBlock) {
if (numOfReplicas == 0 || clusterMap.getNumOfLeaves()==0) {
return (writer instanceof DatanodeDescriptor) ? writer : null;
}
final int numOfResults = results.size();
final int totalReplicasExpected = numOfReplicas + numOfResults;
if ((writer == null || !(writer instanceof DatanodeDescriptor)) && !newBlock) {
writer = results.get(0).getDatanodeDescriptor();
}
// Keep a copy of original excludedNodes
final Set<Node> oldExcludedNodes = new HashSet<Node>(excludedNodes);
// choose storage types; use fallbacks for unavailable storages
final List<StorageType> requiredStorageTypes = storagePolicy
.chooseStorageTypes((short) totalReplicasExpected,
DatanodeStorageInfo.toStorageTypes(results),
unavailableStorages, newBlock);
final EnumMap<StorageType, Integer> storageTypes =
getRequiredStorageTypes(requiredStorageTypes);
if (LOG.isTraceEnabled()) {
LOG.trace("storageTypes=" + storageTypes);
}
try {
if ((numOfReplicas = requiredStorageTypes.size()) == 0) {
throw new NotEnoughReplicasException(
"All required storage types are unavailable: "
+ " unavailableStorages=" + unavailableStorages
+ ", storagePolicy=" + storagePolicy);
}
// 第一个dn的选择,如果client在dn上,则选择此dn,否则随机选dn
if (numOfResults == 0) {
writer = chooseLocalStorage(writer, excludedNodes, blocksize,
maxNodesPerRack, results, avoidStaleNodes, storageTypes, true)
.getDatanodeDescriptor();
if (--numOfReplicas == 0) {
return writer;
}
}
// 从results列表中取出第一个副本所在的dn0
final DatanodeDescriptor dn0 = results.get(0).getDatanodeDescriptor();
// 第二个副本从非dn0的机架上选择一个dn
if (numOfResults <= 1) {
chooseRemoteRack(1, dn0, excludedNodes, blocksize, maxNodesPerRack,
results, avoidStaleNodes, storageTypes);
if (--numOfReplicas == 0) {
return writer;
}
}
if (numOfResults <= 2) {
// 从results中取出第二个副本所在的dn1
final DatanodeDescriptor dn1 = results.get(1).getDatanodeDescriptor();
// 如果dn0和dn1在一个机架上,则从另一个机架中选择一个dn
if (clusterMap.isOnSameRack(dn0, dn1)) {
chooseRemoteRack(1, dn0, excludedNodes, blocksize, maxNodesPerRack,
results, avoidStaleNodes, storageTypes);
} else if (newBlock){
// 如果是新block,即results.isEmpty。
// dn0和dn1不在同一个机架,则从dn1所在的机架上选一个dn
chooseLocalRack(dn1, excludedNodes, blocksize, maxNodesPerRack,
results, avoidStaleNodes, storageTypes);
} else {
// 选择一个同writer同rack的dn
chooseLocalRack(writer, excludedNodes, blocksize, maxNodesPerRack,
results, avoidStaleNodes, storageTypes);
}
if (--numOfReplicas == 0) {
return writer;
}
}
// 超出3个副本的情况,则后面的副本随机选择dn
chooseRandom(numOfReplicas, NodeBase.ROOT, excludedNodes, blocksize,
maxNodesPerRack, results, avoidStaleNodes, storageTypes);
} catch (NotEnoughReplicasException e) {
final String message = "Failed to place enough replicas, still in need of "
+ (totalReplicasExpected - results.size()) + " to reach "
+ totalReplicasExpected
+ " (unavailableStorages=" + unavailableStorages
+ ", storagePolicy=" + storagePolicy
+ ", newBlock=" + newBlock + ")";
if (LOG.isTraceEnabled()) {
LOG.trace(message, e);
} else {
LOG.warn(message + " " + e.getMessage());
}
if (avoidStaleNodes) {
// Retry chooseTarget again, this time not avoiding stale nodes.
// excludedNodes contains the initial excludedNodes and nodes that were
// not chosen because they were stale, decommissioned, etc.
// We need to additionally exclude the nodes that were added to the
// result list in the successful calls to choose*() above.
for (DatanodeStorageInfo resultStorage : results) {
addToExcludedNodes(resultStorage.getDatanodeDescriptor(), oldExcludedNodes);
}
// Set numOfReplicas, since it can get out of sync with the result list
// if the NotEnoughReplicasException was thrown in chooseRandom().
numOfReplicas = totalReplicasExpected - results.size();
return chooseTarget(numOfReplicas, writer, oldExcludedNodes, blocksize,
maxNodesPerRack, results, false, storagePolicy, unavailableStorages,
newBlock);
}
boolean retry = false;
// simply add all the remaining types into unavailableStorages and give
// another try. No best effort is guaranteed here.
for (StorageType type : storageTypes.keySet()) {
if (!unavailableStorages.contains(type)) {
unavailableStorages.add(type);
retry = true;
}
}
if (retry) {
for (DatanodeStorageInfo resultStorage : results) {
addToExcludedNodes(resultStorage.getDatanodeDescriptor(),
oldExcludedNodes);
}
numOfReplicas = totalReplicasExpected - results.size();
return chooseTarget(numOfReplicas, writer, oldExcludedNodes, blocksize,
maxNodesPerRack, results, false, storagePolicy, unavailableStorages,
newBlock);
}
}
return writer;
}
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