Cassandra LCS压缩原理详解-阿里云开发者社区

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Cassandra LCS压缩原理详解

简介: 本篇博文是根据自己阅读Cassandra LCS源码以后,整理的一篇关于LCS的原理博文。

cassandra的压缩的策略是在cassandra的守护线程cassandraDaemon类中的startUp中进行定时启动的压缩机制。
CassandraDaemon setUp()中的定时启动任务
ScheduledExecutors.optionalTasks.scheduleWithFixedDelay(ColumnFamilyStore.getBackgroundCompactionTaskSubmitter(), 5, 1, TimeUnit.MINUTES);

从代码中可以看出,cassandra是启动5分钟以后每隔1分钟就要启动一次压缩任务
public static Runnable getBackgroundCompactionTaskSubmitter()
{

return new Runnable()
{
    public void run()
    {
        for (Keyspace keyspace : Keyspace.all())
            for (ColumnFamilyStore cfs : keyspace.getColumnFamilyStores())
                CompactionManager.instance.submitBackground(cfs);
    }
};

}

从上面的代码中可以看出,获取到所有的keyspace,然后针对所有的keyspace的表进行压缩。
/**

  • Call this whenever a compaction might be needed on the given columnfamily.
  • It's okay to over-call (within reason) if a call is unnecessary, it will
  • turn into a no-op in the bucketing/candidate-scan phase.
    */

public List> submitBackground(final ColumnFamilyStore cfs)
{

if (cfs.isAutoCompactionDisabled()) // 判断表格是否关闭了压缩策略
{
    logger.trace("Autocompaction is disabled");
    return Collections.emptyList();
}

 /**
 * 如果CF当前正在被压缩了,并且没有闲置的线程池了,我们则等待下一次提交当前的CF压缩任务,当我们有足够多线程的时候
 * 否则我们应该至少提交一个任务以防止某个CF长时间霸占线程池,也就是CF饥饿。
 **/
int count = compactingCF.count(cfs);
if (count > 0 && executor.getActiveCount() >= executor.getMaximumPoolSize())
{ // 已经有在压缩了,并且没有空间的线程池,则退出
    logger.trace("Background compaction is still running for {}.{} ({} remaining). Skipping",
                 cfs.keyspace.getName(), cfs.name, count);
    return Collections.emptyList();
}

logger.trace("Scheduling a background task check for {}.{} with {}",
             cfs.keyspace.getName(),
             cfs.name,
             cfs.getCompactionStrategyManager().getName());

List<Future<?>> futures = new ArrayList<>(1);
Future<?> fut = executor.submitIfRunning(new BackgroundCompactionCandidate(cfs), "background task");
//没有正在压缩的,情况,则提交一次压缩,以防止CF 饥饿
if (!fut.isCancelled())
    futures.add(fut);
else
    compactingCF.remove(cfs);
return futures;

}

public void run()
{

try
{
    logger.trace("Checking {}.{}", cfs.keyspace.getName(), cfs.name);
    if (!cfs.isValid()) // 如果已经删除了,则不允许在被压缩了
    {
        logger.trace("Aborting compaction for dropped CF");
        return;
    }
    
    //先从cf表中获取到当前表格的压缩策略
    CompactionStrategyManager strategy = cfs.getCompactionStrategyManager();
    //根据压缩策略,获取到压缩任务,这里需要获取到GC的时间,这里的GC是指墓碑的删除时间
    AbstractCompactionTask task = strategy.getNextBackgroundTask(getDefaultGcBefore(cfs, FBUtilities.nowInSeconds()));
    if (task == null)
    {
        logger.trace("No tasks available");
        return;
    }
    task.execute(metrics);
}
finally
{
    compactingCF.remove(cfs);
}
submitBackground(cfs);

}

/**

  • Return the next background task
    *
  • Returns a task for the compaction strategy that needs it the most (most estimated remaining tasks)
    *

*/
public synchronized AbstractCompactionTask getNextBackgroundTask(int gcBefore)
{

if (!isEnabled())
    return null;

maybeReload(cfs.metadata);

// 将任务分为已经repaired过的,和没有进行repaired的两部分
// 哪个预估剩余的任务量大,就先进行哪个任务
if (repaired.getEstimatedRemainingTasks() > unrepaired.getEstimatedRemainingTasks())
{
    AbstractCompactionTask repairedTask = repaired.getNextBackgroundTask(gcBefore);
    if (repairedTask != null)
        return repairedTask;
    return unrepaired.getNextBackgroundTask(gcBefore);
}
else
{
    AbstractCompactionTask unrepairedTask = unrepaired.getNextBackgroundTask(gcBefore);
    if (unrepairedTask != null)
        return unrepairedTask;
    return repaired.getNextBackgroundTask(gcBefore);
}

}

/**

  • the only difference between background and maximal in LCS is that maximal is still allowed
  • (by explicit user request) even when compaction is disabled.
    */

@SuppressWarnings("resource")
public synchronized AbstractCompactionTask getNextBackgroundTask(int gcBefore)
{

while (true)
{
    OperationType op;
    //获取到压缩的候选者
    LeveledManifest.CompactionCandidate candidate = manifest.getCompactionCandidates();
    if (candidate == null)
    {  // 如果没有压缩候选者,也就是候选者为null
        // 这个时候,没有压缩候选者,那么就尝试针对已经删除的数据,也就是墓碑是否有需要被处理的
        SSTableReader sstable = findDroppableSSTable(gcBefore);
        if (sstable == null)
        {
            logger.trace("No compaction necessary for {}", this);
            return null;
        }
        candidate = new LeveledManifest.CompactionCandidate(Collections.singleton(sstable),
                                                            sstable.getSSTableLevel(),
                                                            getMaxSSTableBytes());
        op = OperationType.TOMBSTONE_COMPACTION;
    }
    else
    {
        op = OperationType.COMPACTION;
    }

    LifecycleTransaction txn = cfs.getTracker().tryModify(candidate.sstables, OperationType.COMPACTION);
    if (txn != null)
    {
        // 返回分层压缩任务
        LeveledCompactionTask newTask = new LeveledCompactionTask(cfs, txn, candidate.level, gcBefore, candidate.maxSSTableBytes, false);
        newTask.setCompactionType(op);
        return newTask;
    }
}

}

/**

  • @return highest-priority sstables to compact, and level to compact them to
  • If no compactions are necessary, will return null
    */

public synchronized CompactionCandidate getCompactionCandidates()
{

// during bootstrap we only do size tiering in L0 to make sure
// the streamed files can be placed in their original levels
if (StorageService.instance.isBootstrapMode())
{
    List<SSTableReader> mostInteresting = getSSTablesForSTCS(getLevel(0));
    if (!mostInteresting.isEmpty())
    {
        logger.info("Bootstrapping - doing STCS in L0");
        return new CompactionCandidate(mostInteresting, 0, Long.MAX_VALUE);
    }
    return null;
}
// LevelDB 会给每个level 一个分数(有多少数据它拥有的比上它的理想数据),并且
// 压缩得分高的层级,但是这样很容以分崩离析,一旦发生落后
// 举个例子,现在L0 有 988个sstable,理想的是4个
// L1 117个sstable,理想的是10个
// L2 12个sstable,理想的是100个
// 问题就是当L0(225) 比 L1(11)要高,那么我们会做一个MAX_COMPACTION_SIZE的L0 和 117个L1压缩
// 并将压缩的结果放到L1,当我们计算下一个L0的时候,又需要一次和L1(120)个sstable一起做压缩
// 这样就会导致L1不停的被压缩,引起频繁的IO读取,而且是指针对L1的。
// 这种压缩策略,一但L0的压缩落后了以后,我们就不得不阻塞写性能
// 因此我们采用不同的策略
// 1. 首先先压缩高层,这样可以最大限度的减少IO
// 2. 并且L0一旦落后比较严重了,会采用SIZE压缩,以减少读性能,从而赶上高层的压缩分数
// 当然这不是一个万全之策,如果一直处于高压的写,也同样会崩溃,但是偶尔爆发性的写,这是一个很好的策略
for (int i = generations.length - 1; i > 0; i--)
{
    List<SSTableReader> sstables = getLevel(i);
    if (sstables.isEmpty())
        continue; // mostly this just avoids polluting the debug log with zero scores
    // we want to calculate score excluding compacting ones
    Set<SSTableReader> sstablesInLevel = Sets.newHashSet(sstables);
    Set<SSTableReader> remaining = Sets.difference(sstablesInLevel, cfs.getTracker().getCompacting());
    // 分数为  sstable的总的大小 /  该层级最大的磁盘空间
    double score = (double) SSTableReader.getTotalBytes(remaining) / (double)maxBytesForLevel(i, maxSSTableSizeInBytes);
    logger.trace("Compaction score for level {} is {}", i, score);

    if (score > 1.001) // 当分数大于1的时候,也就是当前层级的大小比当前c层级最大的允许的磁盘空间
    {
        // 在处理高层级压缩的时候,就需要判断一下L0的层级分数是否落后到足够多以至于开启STCS的压缩
        // before proceeding with a higher level, let's see if L0 is far enough behind to warrant STCS
        CompactionCandidate l0Compaction = getSTCSInL0CompactionCandidate();
        if (l0Compaction != null) // 如果L0 已经落后太多了,开启STCS压缩
            return l0Compaction;

        // L0当前还好,就直接执行当前的压缩策略
        // L0 is fine, proceed with this level
        Collection<SSTableReader> candidates = getCandidatesFor(i);
        if (!candidates.isEmpty())
        {
            int nextLevel = getNextLevel(candidates);
            // 将它的上一级的压缩次数清0,并且判断是否存在饥饿压缩的情况,如果是的话,就要考虑一下 sstable是否和候选者之间存在重叠,并且没有在压缩
            // 则也需要一起加进来就行一起压缩,这主要原因是因为有些层级数据量太少了,一直灭有被压缩过
            candidates = getOverlappingStarvedSSTables(nextLevel, candidates);
            if (logger.isTraceEnabled())
                logger.trace("Compaction candidates for L{} are {}", i, toString(candidates));
            return new CompactionCandidate(candidates, nextLevel, cfs.getCompactionStrategyManager().getMaxSSTableBytes());
        }
        else
        {
            logger.trace("No compaction candidates for L{}", i);
        }
    }
}

// Higher levels are happy, time for a standard, non-STCS L0 compaction
if (getLevel(0).isEmpty())
    return null;
Collection<SSTableReader> candidates = getCandidatesFor(0);
if (candidates.isEmpty())  // 如果获取到的L0层级的压缩候选者数据量为0,则直接进行stcs压缩
{
    // Since we don't have any other compactions to do, see if there is a STCS compaction to perform in L0; if
    // there is a long running compaction, we want to make sure that we continue to keep the number of SSTables
    // small in L0.
    return getSTCSInL0CompactionCandidate();
}
return new CompactionCandidate(candidates, getNextLevel(candidates), cfs.getCompactionStrategyManager().getMaxSSTableBytes());

}

/**

  • @return highest-priority sstables to compact for the given level.
  • If no compactions are possible (because of concurrent compactions or because some sstables are blacklisted
  • for prior failure), will return an empty list. Never returns null.
    */

private Collection getCandidatesFor(int level)
{

assert !getLevel(level).isEmpty();
logger.trace("Choosing candidates for L{}", level);

final Set<SSTableReader> compacting = cfs.getTracker().getCompacting();

if (level == 0) // 如果是level为0就走level 0 的压缩策略
{
    // 先要获取到L0正在压缩的sstable
    Set<SSTableReader> compactingL0 = getCompacting(0);

    // 首选,先要获取到L0 正在压缩的sstable中最大的 parttion 
    // 和最小的partion
    PartitionPosition lastCompactingKey = null;
    PartitionPosition firstCompactingKey = null;
    for (SSTableReader candidate : compactingL0)
    {
        if (firstCompactingKey == null || candidate.first.compareTo(firstCompactingKey) < 0)
            firstCompactingKey = candidate.first;
        if (lastCompactingKey == null || candidate.last.compareTo(lastCompactingKey) > 0)
            lastCompactingKey = candidate.last;
    }

    // L0 是很多新得sstable的垃圾场,因此可能会存在很多的sstable重叠
    // 我们对待L0的压缩比较特殊
    // 1. 添加sstables到 候选者集合中,直到至少最大的数量
    // 2. 优先选择老的sstable,而不是新的sstable,并且任意和候选者只有
    // 重叠的sstable也都会加入熬后选择中,当L0的sstable的数量大于Max的时候
    // 就会发起压缩
    // 如果所有的候选者的大小小于最大MB的时候,我们将不会打扰L1层,并
    // 将压缩后的结果保存到L0中,而不是直接提升。
    // L0 is the dumping ground for new sstables which thus may overlap each other.
    //
    // We treat L0 compactions specially:
    // 1a. add sstables to the candidate set until we have at least maxSSTableSizeInMB
    // 1b. prefer choosing older sstables as candidates, to newer ones
    // 1c. any L0 sstables that overlap a candidate, will also become candidates
    // 2. At most MAX_COMPACTING_L0 sstables from L0 will be compacted at once
    // 3. If total candidate size is less than maxSSTableSizeInMB, we won't bother compacting with L1,
    //    and the result of the compaction will stay in L0 instead of being promoted (see promote())
    //
    // Note that we ignore suspect-ness of L1 sstables here, since if an L1 sstable is suspect we're
    // basically screwed, since we expect all or most L0 sstables to overlap with each L1 sstable.
    // So if an L1 sstable is suspect we can't do much besides try anyway and hope for the best.
    Set<SSTableReader> candidates = new HashSet<>();
    Set<SSTableReader> remaining = new HashSet<>();
    //任何可疑的sstable
    Iterables.addAll(remaining, Iterables.filter(getLevel(0), Predicates.not(suspectP)));
    // 将剩余的可疑的sstable按照sstable生成的时间进行排序
    for (SSTableReader sstable : ageSortedSSTables(remaining))
    {
        // 如果已经在候选者中了,就直接跳过
        if (candidates.contains(sstable))
            continue;

        //剩余的sstable和当前的sstalec有重叠的部分也会被加如到候选者中
        // 这里的重叠指得时 sstable中得最大最小得token。也就是说
        // 任何sstable 和 当前得sstable得token之间存在交集,也就是范围存在交集
        // 这里可能认为token范围重叠,就存在内容重叠吧?
        Sets.SetView<SSTableReader> overlappedL0 = Sets.union(Collections.singleton(sstable), overlapping(sstable, remaining));
        if (!Sets.intersection(overlappedL0, compactingL0).isEmpty())
            continue;  // 如果所有重叠额sstable和当前得sstable一起,和正在压缩得sstable之间存在交集,则直接跳
        // 如果overlappedL0 没有正在压缩的sstable,则需要判断
        // 候选者中是否有和正在压缩的l0层sstable 有token范围交集
        // 如果没有交集,则认为当前的sstable就直接加入候选者
        // 
        for (SSTableReader newCandidate : overlappedL0)
        {
            if (firstCompactingKey == null || lastCompactingKey == null || overlapping(firstCompactingKey.getToken(), lastCompactingKey.getToken(), Arrays.asList(newCandidate)).size() == 0)
                candidates.add(newCandidate);
            remaining.remove(newCandidate); // 已经经过重叠的sstable就不在进行重复添加了
            // 要么这个sstable 和 正在压缩的有重叠,要么已经加入到候选者,所以可以在剩余的sstable集合中直接删除
        }

        //如果候选者的数据已经大于MAX_COMPACTING_L0的时候,直接获取到时间最早的最大数据量的sstable
        if (candidates.size() > MAX_COMPACTING_L0)
        {
            // limit to only the MAX_COMPACTING_L0 oldest candidates
            candidates = new HashSet<>(ageSortedSSTables(candidates).subList(0, MAX_COMPACTING_L0));
            break;
        }
    }
    
    // 如果候选者加起来的sstable的大小比最大值的话,就需要加入L1层中的sstable进来一起压缩
    // leave everything in L0 if we didn't end up with a full sstable's worth of data
    if (SSTableReader.getTotalBytes(candidates) > maxSSTableSizeInBytes)
    {
        // add sstables from L1 that overlap candidates
        // if the overlapping ones are already busy in a compaction, leave it out.
        // TODO try to find a set of L0 sstables that only overlaps with non-busy L1 sstables
        // 候选者最大最小的tokenf范围内和L1有重叠的sstable
        Set<SSTableReader> l1overlapping = overlapping(candidates, getLevel(1));
        // L1重叠的sstable和正在压缩的sstable有重叠,则直接放弃当前的L0压缩
        if (Sets.intersection(l1overlapping, compacting).size() > 0)
            return Collections.emptyList();
        // 如果L0正在压缩的sstable 和 候选者之间存在token重叠的话,也直接放弃当前L0压缩    
        if (!overlapping(candidates, compactingL0).isEmpty())
            return Collections.emptyList();
        candidates = Sets.union(candidates, l1overlapping);
    }
    if (candidates.size() < 2)
        return Collections.emptyList();
    else
        return candidates;
}

// for non-L0 compactions, pick up where we left off last time
Collections.sort(getLevel(level), SSTableReader.sstableComparator);
int start = 0; // handles case where the prior compaction touched the very last range
for (int i = 0; i < getLevel(level).size(); i++)
{
    SSTableReader sstable = getLevel(level).get(i);
    if (sstable.first.compareTo(lastCompactedKeys[level]) > 0)
    {
        start = i;
        break;
    }
}

// look for a non-suspect keyspace to compact with, starting with where we left off last time,
// and wrapping back to the beginning of the generation if necessary
for (int i = 0; i < getLevel(level).size(); i++)
{
    SSTableReader sstable = getLevel(level).get((start + i) % getLevel(level).size());
    Set<SSTableReader> candidates = Sets.union(Collections.singleton(sstable), overlapping(sstable, getLevel(level + 1)));
    if (Iterables.any(candidates, suspectP))
        continue;
    if (Sets.intersection(candidates, compacting).isEmpty())
        return candidates;
}

// all the sstables were suspect or overlapped with something suspect
return Collections.emptyList();

}

private CompactionCandidate getSTCSInL0CompactionCandidate()
{

if (!DatabaseDescriptor.getDisableSTCSInL0() && getLevel(0).size() > MAX_COMPACTING_L0)
{
    List<SSTableReader> mostInteresting = getSSTablesForSTCS(getLevel(0));
    if (!mostInteresting.isEmpty())
    {
        logger.debug("L0 is too far behind, performing size-tiering there first");
        return new CompactionCandidate(mostInteresting, 0, Long.MAX_VALUE);
    }
}

return null;

}

private CompactionCandidate getSTCSInL0CompactionCandidate()
{

//  如果开启了STCS压缩,并且L0的sstable 的总数大于 MAX量,则开启STCS压缩
// 
if (!DatabaseDescriptor.getDisableSTCSInL0() && getLevel(0).size() > MAX_COMPACTING_L0)
{
    List<SSTableReader> mostInteresting = getSSTablesForSTCS(getLevel(0));
    if (!mostInteresting.isEmpty())
    {
        logger.debug("L0 is too far behind, performing size-tiering there first");
        return new CompactionCandidate(mostInteresting, 0, Long.MAX_VALUE);
    }
}

return null;

}

STCS的大小归类的方法是,比如1 2 sstable的平均大小作为一个值,这个值上上下 0.5倍也都加入
到这个sstable中,然后再进行求解 平均值,然后再重新计算上下值,加入到这个sstable中,进行重新编写大小。最后返回大小差不多的sstable加入到一起
然后比较所有大小差不多的 sstable 集合,之间所有的sstable的热度比较大小,返回热度最大的sstable集合进行压缩。因为读取越多的sstable,优先进行压缩,有利于提升读性能
private List getSSTablesForSTCS(Collection sstables)
{

Iterable<SSTableReader> candidates = cfs.getTracker().getUncompacting(sstables);
List<Pair<SSTableReader,Long>> pairs = SizeTieredCompactionStrategy.createSSTableAndLengthPairs(AbstractCompactionStrategy.filterSuspectSSTables(candidates));
List<List<SSTableReader>> buckets = SizeTieredCompactionStrategy.getBuckets(pairs,
                                                                            options.bucketHigh,
                                                                            options.bucketLow,
                                                                            options.minSSTableSize);
return SizeTieredCompactionStrategy.mostInterestingBucket(buckets, 4, 32);

}

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