简介
本文是需要用到hbase timestamp性质时研究源码所写.内容有一定侧重.且个人理解不算深入,如有错误请不吝指出.
如何看源码
hbase依赖很重,没有独立的client包.所以目前如果在maven中指定如下:
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase</artifactId>
<version>0.94-adh3u9.9</version>
<exclusions>
<exclusion>
<groupId>org.jruby</groupId>
<artifactId>jruby-complete</artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
</exclusion>
</exclusions>
</dependency>可以看到其会把整个hbase的源码都下载下来.这一点在查看源码上是比较方便的.
入口
本文以get为例.代码的入口位于org.apache.hadoop.hbase.client.HTable的get方法:
public Result get(final Get get) throws IOException {
try {
startTrace(get);
ServerCallable<Result> serverCallable = new ServerCallable<Result>(
connection, tableName, get.getRow(), operationTimeout) {
public Result call() throws IOException {
// rpc调用服务端进行查询
return server.get(location.getRegionInfo().getRegionName(), get);
}
};
return executeServerCallable(serverCallable);
} finally {
endTrace(TableOperationMetricType.GET, 1);
}
}其中在实际执行server.get时,会通过反射调用一个rpc接口真正和服务器进行沟通.所以如果把断点打在这个函数的里面,会发现无法断住.
路由
这里其实有一个很重要的问题.就是在执行get的时候.用户只会传入rowkey等信息,这里hbase是如何根据rowkey确认该数据所在region的.由上述代码可见location.getRegionInfo().getRegionName()即获取到了regionname.这一块的细节逻辑未深入研究.
regionserver逻辑
public Result get(byte[] regionName, Get get) throws IOException {
checkOpen();
final long startTime = System.nanoTime();
ReadMetricsData metricsData = null;
try {
// 确认当前请求真正的region
HRegion region = getRegion(regionName);
checkReadEnabled(region.getTableDesc().getNameAsString());
HBaseServer.setRegionInfoForCurCall(region.getRegionInfo()
.getTableNameAsString(), region.getRegionInfo().getEncodedName());
if (!region.getRegionInfo().isMetaTable()) {
metricsData = new ReadMetricsData();
ReadMetricsData.setCurReadMetricsData(metricsData);
}
// 真正执行查询,第二个参数是一个递增的整数.用于实现mvcc,即多版本并发控制
// 简单描述就是查询时会依靠这个数字来确定读取的数据版本,避免出现读取put
// 多个列、列族时,get到其插入一半的数据.
Result r = region.get(get, getLockFromId(get.getLockId()));
if (get.isSupportEncodingResult()) {
r.setKVEncoding(KVEncoding.FASTPREFIX);
}
int dataLen = (int) r.getWritableSize();
long took = System.nanoTime() - startTime;
this.addReadMetricsCount(region, dataLen,
HBaseServer.getRemoteAddress(), 1, (int) (took / MS_CONVERTTO_NS));
if (metricsData != null) {
this.metrics.getLatencies.updateReadMetricsData(metricsData, took);
}
return r;
} catch (Throwable t) {
this.metrics.failedReadRequests.inc();
throw convertThrowableToIOE(cleanup(t));
} finally {
ReadMetricsData.setCurReadMetricsData(null);
}
}region逻辑
上文代码最终会调用:HRegion.get(Get get, boolean withCoprocessor)方法.
private List<KeyValue> get(Get get, boolean withCoprocessor)
throws IOException {
long now = EnvironmentEdgeManager.currentTimeMillis();
List<KeyValue> results = new ArrayList<KeyValue>();
// pre-get CP hook
// Coprocessor 为hbase中的协处理器概念.其可由clinet发往hbaseserver,在执行put、get前后
// 执行一些逻辑.如进行简单运算、筛选.
if (withCoprocessor && (coprocessors != null)) {
if (coprocessors.preGet(get, results)) {
return results;
}
}
// 注意这里.所有的get都会转化为一个scan
Scan scan = new Scan(get);
RegionScanner scanner = null;
try {
// 构造scanner
scanner = getScanner(scan);
// 从scanner中取出数据放入results
scanner.next(results);
} finally {
if (scanner != null)
scanner.close();
}
// post-get CP hook
// 协处理器的后置处理调用
if (withCoprocessor && (coprocessors != null)) {
coprocessors.postGet(get, results);
}
// do after lock
final long after = EnvironmentEdgeManager.currentTimeMillis();
this.opMetrics.updateGetMetrics(get.familySet(), after - now);
return results;
}对于Scan scan = new Scan(get);,在scan的构造方法中可见:
public Scan(Get get) {
this.startRow = get.getRow();
this.stopRow = get.getRow();
this.filter = get.getFilter();
this.cacheBlocks = get.getCacheBlocks();
this.maxVersions = get.getMaxVersions();
this.tr = get.getTimeRange();
this.familyMap = get.getFamilyMap();
}也就是对于一个get.实际上是把其当做一个scan进行查询的.所以这里可以推断出.一个get和一个startrow、stoprow均相同的scan,在执行效率上是不会有差异的.
其次是关于timerange这一项.hbase中一个get、scan.可以设置timerange或者timestamp.其中timerange是指只查询某个时间范围的数据.而timestamp是指只查询某个时间点的数据.
而如果不设置,则会默认查询所有数据.这一块的逻辑实现就在get、scan的构造方法和set方法中.
- 如果用户没有setTimeStamp、setTimeRange
// get、scan均会调用默认无参数构造方法构造其tr.
private TimeRange tr = new TimeRange();- 如果用户进行了设置
public Get setTimeRange(long minStamp, long maxStamp)
throws IOException {
tr = new TimeRange(minStamp, maxStamp);
return this;
}
public Get setTimeStamp(long timestamp) {
try {
tr = new TimeRange(timestamp, timestamp+1);
} catch(IOException e) {
// Will never happen
}
return this;
}可见timestamp是一种特殊的timerange,其构造方法为[timestamp,timestamp+1)的range.
scanner存在的意义
hbase的scanner并非为了使用设计模式而强行加入一个scanner做数据查询.这里的scanner的必要性主要在于其逻辑、物理存储特性.这里简单描述就是,在hbase中一个region是由多个store的.每个store才是真正的存储的逻辑最小单元.而一个store里面又有一个memstore(内存),零个或多个storefile(一般是位于HDFS的HFile文件).
由此,有个很明显的问题:在用户的一次查询中,用户的输入是一个rowkey,而这个rowkey的不同列族的数据,可能在不同的store中.而即使确定了一个store,可能数据在memstore中(尚未flush到硬盘),也有可能已经在storefile中了.
进一步也就需要一个机制从这些不同的逻辑、物理的存储媒介中遍历查询数据并且做一个合并.
scanner 的构造
暂无
scanner的遍历逻辑
上文中的scanner.next(results);最终会执行到HRegion.nextInternal(int limit)方法.
代码:
private boolean nextInternal(int limit) throws IOException {
RpcCallContext rpcCall = HBaseServer.getCurrentCall();
while (true) {
if (rpcCall != null) {
// If a user specifies a too-restrictive or too-slow scanner, the
// client might time out and disconnect while the server side
// is still processing the request. We should abort aggressively
// in that case.
rpcCall.throwExceptionIfCallerDisconnected();
}
KeyValue current = this.storeHeap.peek();
byte[] currentRow = null;
int offset = 0;
short length = 0;
if (current != null) {
currentRow = current.getBuffer();
offset = current.getRowOffset();
length = current.getRowLength();
}
if (isStopRow(currentRow, offset, length)) {
if (filter != null && filter.hasFilterRow()) {
filter.filterRow(results);
}
if (filter != null && filter.filterRow()) {
results.clear();
}
return false;
} else if (filterRowKey(currentRow, offset, length)) {
nextRow(currentRow, offset, length);
} else {
KeyValue nextKv;
do {
this.storeHeap.next(results, limit - results.size());
if (limit > 0 && results.size() == limit) {
if (this.filter != null && filter.hasFilterRow()) {
throw new IncompatibleFilterException(
"Filter with filterRow(List<KeyValue>) incompatible with scan with limit!");
}
return true; // we are expecting more yes, but also limited to how many we can return.
}
nextKv = this.storeHeap.peek();
} while (nextKv != null && nextKv.matchingRow(currentRow, offset, length));
final boolean stopRow = nextKv == null || isStopRow(nextKv.getBuffer(), nextKv.getRowOffset(), nextKv.getRowLength());
// now that we have an entire row, lets process with a filters:
// first filter with the filterRow(List)
if (filter != null && filter.hasFilterRow()) {
filter.filterRow(results);
}
if (results.isEmpty() || filterRow()) {
// this seems like a redundant step - we already consumed the row
// there're no left overs.
// the reasons for calling this method are:
// 1. reset the filters.
// 2. provide a hook to fast forward the row (used by subclasses)
nextRow(currentRow, offset, length);
// This row was totally filtered out, if this is NOT the last row,
// we should continue on.
if (!stopRow) continue;
} else if (this.remainingOffset > 0) {
this.remainingOffset--;
nextRow(currentRow, offset, length);
if (!stopRow) continue;
}
return !stopRow;
}
}
}简化版:
private boolean nextInternal(int limit) throws IOException {
while (true) {
// 获取heap顶的KeyValue
KeyValue current = this.storeHeap.peek();
byte[] currentRow = null;
int offset = 0;
short length = 0;
if (current != null) {
currentRow = current.getBuffer();
offset = current.getRowOffset();
length = current.getRowLength();
}
if (isStopRow(currentRow, offset, length)) {
// 如果是结束行
return false;
} else if (filterRowKey(currentRow, offset, length)) {
// 如果filter过滤掉了.则直接看下一行数据
nextRow(currentRow, offset, length);
} else {
KeyValue nextKv;
do {
// 核心代码
this.storeHeap.next(results, limit - results.size());
nextKv = this.storeHeap.peek();
} while (nextKv != null && nextKv.matchingRow(currentRow, offset, length));
}
}
}这里省去了边缘控制、过滤逻辑等内容.主要关注其核心逻辑.
全部代码中最核心的有两行:KeyValue current = this.storeHeap.peek();和this.storeHeap.next(results, limit - results.size());.
这里的storeHeap是hbase维护的一个二叉堆(优先队列).这个堆里面存储的元素,是scanner.每个scanner都会持有当前scanner目前最新的、尚未返回的keyvalue.这个二叉堆的排序方式就是根据每个scanner当前keyvalue的rowkey进行排序.
每次执行查询的时候.首先会用KeyValue current = this.storeHeap.peek();取出堆顶的scanner的当前keyvalue.进行一些逻辑判断(主要是判断rowkey,如判断是否超过limit、是否到了stoprow、是否被filter过滤等).如果该keyvalue全部通过,也就是认为其应该被本次查询查到.会执行一次this.storeHeap.next(results, limit - results.size());注意这里才是有可能把当前keyvalue放入查询结果的地方.(不一定会放入,next方法中还有针对value的判断逻辑,比如比较timestamp是否正确).
scanner的排序
上文提到了scanner会依靠其当前元素rowkey进行排序.可以在类KeyValueHeap的构造方法中看到端倪.
KeyValueHeap(List<? extends KeyValueScanner> scanners,
KVScannerComparator comparator) throws IOException {
this.comparator = comparator;
if (!scanners.isEmpty()) {
this.heap = new PriorityQueue<KeyValueScanner>(scanners.size(),
this.comparator);
for (KeyValueScanner scanner : scanners) {
if (scanner.peek() != null) {
this.heap.add(scanner);
} else {
scanner.close();
}
}
this.current = pollRealKV();
}
}毫无疑问.从scanner中取出元素也会影响KeyValueHeap中二叉堆的排序.故其可以保证二叉堆的堆顶的scanner的当前keyvalue一直是离上个遍历到的rowkey最近的keyvalue.
二叉堆的next逻辑
上文中的this.storeHeap.next(results, limit - results.size());最终会执行到:StoreScanner.next(List<KeyValue> outResult, int limit).其源码很长:
public synchronized boolean next(List<KeyValue> outResult, int limit) throws IOException {
if (checkReseek()) {
return true;
}
// if the heap was left null, then the scanners had previously run out anyways, close and
// return.
if (this.heap == null) {
close();
return false;
}
KeyValue peeked = this.heap.peek();
if (peeked == null) {
close();
return false;
}
// only call setRow if the row changes; avoids confusing the query matcher
// if scanning intra-row
byte[] row = peeked.getBuffer();
int offset = peeked.getRowOffset();
short length = peeked.getRowLength();
if ((matcher.row == null) || !Bytes.equals(row, offset, length, matcher.row, matcher.rowOffset, matcher.rowLength)) {
matcher.setRow(row, offset, length);
}
KeyValue kv;
KeyValue prevKV = null;
List<KeyValue> results = new ArrayList<KeyValue>();
// Only do a sanity-check if store and comparator are available.
KeyValue.KVComparator comparator =
store != null ? store.getComparator() : null;
long cumulativeMetric = 0;
try {
LOOP: while ((kv = this.heap.peek()) != null) {
// Check that the heap gives us KVs in an increasing order.
checkScanOrder(prevKV, kv, comparator);
prevKV = kv;
ScanQueryMatcher.MatchCode qcode = matcher.match(kv);
switch (qcode) {
case INCLUDE:
case INCLUDE_AND_SEEK_NEXT_ROW:
case INCLUDE_AND_SEEK_NEXT_COL:
Filter f = matcher.getFilter();
results.add(f == null ? kv : f.transform(kv));
if (limit > 0 && results.size() == limit) {
if (this.mustIncludeColumn != null) {
throw new DoNotRetryIOException("Assistant " + this.assistant
+ " incompatible with scan where limit=" + limit);
}
}
if (qcode == ScanQueryMatcher.MatchCode.INCLUDE_AND_SEEK_NEXT_ROW) {
if (!matcher.moreRowsMayExistAfter(kv)) {
filterRowIfMissingMustIncludeColumn(results);
outResult.addAll(results);
return false;
}
seekToNextRow(kv);
} else if (qcode == ScanQueryMatcher.MatchCode.INCLUDE_AND_SEEK_NEXT_COL) {
reseek(matcher.getKeyForNextColumn(kv));
} else {
this.heap.next();
}
cumulativeMetric += kv.getLength();
if (limit > 0 && (results.size() == limit)) {
break LOOP;
}
continue;
case DONE:
filterRowIfMissingMustIncludeColumn(results);
// copy jazz
outResult.addAll(results);
return true;
case DONE_SCAN:
close();
filterRowIfMissingMustIncludeColumn(results);
// copy jazz
outResult.addAll(results);
return false;
case SEEK_NEXT_ROW:
// This is just a relatively simple end of scan fix, to
// short-cut end
// us if there is an endKey in the scan.
if (!matcher.moreRowsMayExistAfter(kv)) {
filterRowIfMissingMustIncludeColumn(results);
outResult.addAll(results);
return false;
}
seekToNextRow(kv);
break;
case SEEK_NEXT_COL:
reseek(matcher.getKeyForNextColumn(kv));
break;
case SKIP:
this.heap.next();
break;
case SEEK_NEXT_USING_HINT:
KeyValue nextKV = matcher.getNextKeyHint(kv);
if (nextKV != null) {
reseek(nextKV);
} else {
heap.next();
}
break;
default:
throw new RuntimeException("UNEXPECTED");
}
}
} finally {
RegionMetricsStorage.incrNumericMetric(metricNameGetSize,
cumulativeMetric);
}
if (!results.isEmpty()) {
filterRowIfMissingMustIncludeColumn(results);
// copy jazz
outResult.addAll(results);
return true;
}
// No more keys
close();
return false;
}简化版:
public synchronized boolean next(List<KeyValue> outResult, int limit) throws IOException {
KeyValue peeked = this.heap.peek();
if (peeked == null) {
close();
return false;
}
byte[] row = peeked.getBuffer();
int offset = peeked.getRowOffset();
short length = peeked.getRowLength();
if ((matcher.row == null) || !Bytes.equals(row, offset, length, matcher.row, matcher.rowOffset, matcher.rowLength)) {
matcher.setRow(row, offset, length);
}
KeyValue kv;
List<KeyValue> results = new ArrayList<KeyValue>();
KeyValue.KVComparator comparator =
store != null ? store.getComparator() : null;
long cumulativeMetric = 0;
try {
// 获取本store当前的keyvalue
LOOP: while ((kv = this.heap.peek()) != null) {
// 执行匹配.这里的匹配规则部分从用户的filter中来
ScanQueryMatcher.MatchCode qcode = matcher.match(kv);
switch (qcode) {
case INCLUDE:
case INCLUDE_AND_SEEK_NEXT_ROW:
case INCLUDE_AND_SEEK_NEXT_COL:
// 针对当前keyvalue应该包含在用户的查询结果的情况
Filter f = matcher.getFilter();
results.add(f == null ? kv : f.transform(kv));
if (qcode == ScanQueryMatcher.MatchCode.INCLUDE_AND_SEEK_NEXT_ROW) {
if (!matcher.moreRowsMayExistAfter(kv)) {
filterRowIfMissingMustIncludeColumn(results);
outResult.addAll(results);
return false;
}
seekToNextRow(kv);
} else if (qcode == ScanQueryMatcher.MatchCode.INCLUDE_AND_SEEK_NEXT_COL) {
reseek(matcher.getKeyForNextColumn(kv));
} else {
this.heap.next();
}
cumulativeMetric += kv.getLength();
if (limit > 0 && (results.size() == limit)) {
// 查到了limit的数量.跳出循环
break LOOP;
}
continue;
case DONE:
case DONE_SCAN:
case SEEK_NEXT_ROW:
case SEEK_NEXT_COL:
case SKIP:
case SEEK_NEXT_USING_HINT:
// 这些都是查询过程中的各种情况.会有针对的处理
default:
throw new RuntimeException("UNEXPECTED");
}
}
} finally {
RegionMetricsStorage.incrNumericMetric(metricNameGetSize,
cumulativeMetric);
}
// 放入结果中
if (!results.isEmpty()) {
filterRowIfMissingMustIncludeColumn(results);
outResult.addAll(results);
return true;
}
close();
return false;
}上述代码可以看到整个next的核心逻辑.其实就是把当前store的当前keyvalue取出.用一个matcher做比较.看看该数据是否应该是用户查询的结果.
查询到的数据的多种可能
上文中的枚举值.这里举个例子.比如查询到一行数据的第一列是应该被查询到的.按理就应该查询其列族的第二列.而如果发现该行数据被删除掉了.那么不用查询其第二列了.也不用查询下个store了.应该直接查询下一行数据.
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