最近有个需求要对mysql的全量数据迁移到hbase,虽然hbase的设计非常利于高效的读取,但是它的compaction实现对海量数据写入造成非常大的影响,数据到一定量之后,就开始抽风。
分析hbase的实现,不管其运行的机制,其最终存储结构为分布式文件系统中的hfile格式。
刚好hbase的源代码中提供一个HFileOutputFormat类,分析其源代码可以看到:
可以看到,它的工作流程就是首先根据你的配置文件初始化,然后写成hfile的格式。
这里我做了个偷懒的demo:
执行然之后,会在hdfs的/tmp目录下生成一份文件。注意批量写数据的时候一定要保证key的有序性
这个时候,hbase自己提供的一个基于jruby的loadtable.rb脚本就可以发挥作用了。
它的格式是loadtable.rb 你希望的表明 hdfs路径:
hbase org.jruby.Main loadtable.rb offer hdfs://user/root/importoffer/_temporary/_attempt__0000_r_000000_0/
执行完之后:
运行./hbase shell
>list
就会显示刚才导入的offer表了。
分析hbase的实现,不管其运行的机制,其最终存储结构为分布式文件系统中的hfile格式。
刚好hbase的源代码中提供一个HFileOutputFormat类,分析其源代码可以看到:
- /**
- * Copyright 2009 The Apache Software Foundation
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
- package org.apache.hadoop.hbase.mapreduce;
- import java.io.IOException;
- import java.util.Map;
- import java.util.TreeMap;
- import org.apache.hadoop.conf.Configuration;
- import org.apache.hadoop.fs.FileSystem;
- import org.apache.hadoop.fs.Path;
- import org.apache.hadoop.hbase.HConstants;
- import org.apache.hadoop.hbase.KeyValue;
- import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
- import org.apache.hadoop.hbase.io.hfile.Compression;
- import org.apache.hadoop.hbase.io.hfile.HFile;
- import org.apache.hadoop.hbase.regionserver.StoreFile;
- import org.apache.hadoop.hbase.util.Bytes;
- import org.apache.hadoop.mapreduce.RecordWriter;
- import org.apache.hadoop.mapreduce.TaskAttemptContext;
- import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter;
- import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
- import org.mortbay.log.Log;
- /**
- * Writes HFiles. Passed KeyValues must arrive in order.
- * Currently, can only write files to a single column family at a
- * time. Multiple column families requires coordinating keys cross family.
- * Writes current time as the sequence id for the file. Sets the major compacted
- * attribute on created hfiles.
- * @see KeyValueSortReducer
- */
- public class HFileOutputFormat extends FileOutputFormat<ImmutableBytesWritable, KeyValue> {
- public RecordWriter<ImmutableBytesWritable, KeyValue> getRecordWriter(TaskAttemptContext context)
- throws IOException, InterruptedException {
- // Get the path of the temporary output file
- final Path outputPath = FileOutputFormat.getOutputPath(context);
- final Path outputdir = new FileOutputCommitter(outputPath, context).getWorkPath();
- Configuration conf = context.getConfiguration();
- final FileSystem fs = outputdir.getFileSystem(conf);
- // These configs. are from hbase-*.xml
- final long maxsize = conf.getLong("hbase.hregion.max.filesize", 268435456);
- final int blocksize = conf.getInt("hfile.min.blocksize.size", 65536);
- // Invented config. Add to hbase-*.xml if other than default compression.
- final String compression = conf.get("hfile.compression",
- Compression.Algorithm.NONE.getName());
- return new RecordWriter<ImmutableBytesWritable, KeyValue>() {
- // Map of families to writers and how much has been output on the writer.
- private final Map<byte [], WriterLength> writers =
- new TreeMap<byte [], WriterLength>(Bytes.BYTES_COMPARATOR);
- private byte [] previousRow = HConstants.EMPTY_BYTE_ARRAY;
- private final byte [] now = Bytes.toBytes(System.currentTimeMillis());
- public void write(ImmutableBytesWritable row, KeyValue kv)
- throws IOException {
- long length = kv.getLength();
- byte [] family = kv.getFamily();
- WriterLength wl = this.writers.get(family);
- if (wl == null || ((length + wl.written) >= maxsize) &&
- Bytes.compareTo(this.previousRow, 0, this.previousRow.length,
- kv.getBuffer(), kv.getRowOffset(), kv.getRowLength()) != 0) {
- // Get a new writer.
- Path basedir = new Path(outputdir, Bytes.toString(family));
- if (wl == null) {
- wl = new WriterLength();
- this.writers.put(family, wl);
- if (this.writers.size() > 1) throw new IOException("One family only");
- // If wl == null, first file in family. Ensure family dir exits.
- if (!fs.exists(basedir)) fs.mkdirs(basedir);
- }
- wl.writer = getNewWriter(wl.writer, basedir);
- Log.info("Writer=" + wl.writer.getPath() +
- ((wl.written == 0)? "": ", wrote=" + wl.written));
- wl.written = 0;
- }
- kv.updateLatestStamp(this.now);
- wl.writer.append(kv);
- wl.written += length;
- // Copy the row so we know when a row transition.
- this.previousRow = kv.getRow();
- }
- /* Create a new HFile.Writer. Close current if there is one.
- * @param writer
- * @param familydir
- * @return A new HFile.Writer.
- * @throws IOException
- */
- private HFile.Writer getNewWriter(final HFile.Writer writer,
- final Path familydir)
- throws IOException {
- close(writer);
- return new HFile.Writer(fs, StoreFile.getUniqueFile(fs, familydir),
- blocksize, compression, KeyValue.KEY_COMPARATOR);
- }
- private void close(final HFile.Writer w) throws IOException {
- if (w != null) {
- StoreFile.appendMetadata(w, System.currentTimeMillis(), true);
- w.close();
- }
- }
- public void close(TaskAttemptContext c)
- throws IOException, InterruptedException {
- for (Map.Entry<byte [], WriterLength> e: this.writers.entrySet()) {
- close(e.getValue().writer);
- }
- }
- };
- }
- /*
- * Data structure to hold a Writer and amount of data written on it.
- */
- static class WriterLength {
- long written = 0;
- HFile.Writer writer = null;
- }
- }
可以看到,它的工作流程就是首先根据你的配置文件初始化,然后写成hfile的格式。
这里我做了个偷懒的demo:
- HFileOutputFormat hf = new HFileOutputFormat();
- HBaseConfiguration conf = new HBaseConfiguration();
- conf.addResource(new Path("/home/performance/softs/hadoop/conf/core-site.xml"));
- conf.set("mapred.output.dir", "/tmp");
- conf.set("hfile.compression", Compression.Algorithm.LZO.getName());
- TaskAttemptContext context = new TaskAttemptContext(conf, new TaskAttemptID());
- RecordWriter writer = hf.getRecordWriter(context);
- KeyValue kv = new KeyValue(Bytes.toBytes("1111111111111"), Bytes.toBytes("offer:action"),
- System.currentTimeMillis(), Bytes.toBytes("test"));
- KeyValue kv1 = new KeyValue(Bytes.toBytes("1111111111111"), Bytes.toBytes("offer:id"),
- System.currentTimeMillis(), Bytes.toBytes("123"));
- KeyValue kv3 = new KeyValue(Bytes.toBytes("1111111111112"), Bytes.toBytes("offer:action"),
- System.currentTimeMillis(), Bytes.toBytes("test"));
- KeyValue kv4 = new KeyValue(Bytes.toBytes("1111111111112"), Bytes.toBytes("offer:id"),
- System.currentTimeMillis(), Bytes.toBytes("123"));
- writer.write(null, kv);
- writer.write(null, kv1);
- writer.write(null, kv3);
- writer.write(null, kv4);
- writer.close(context);
执行然之后,会在hdfs的/tmp目录下生成一份文件。注意批量写数据的时候一定要保证key的有序性
这个时候,hbase自己提供的一个基于jruby的loadtable.rb脚本就可以发挥作用了。
它的格式是loadtable.rb 你希望的表明 hdfs路径:
hbase org.jruby.Main loadtable.rb offer hdfs://user/root/importoffer/_temporary/_attempt__0000_r_000000_0/
执行完之后:
运行./hbase shell
>list
就会显示刚才导入的offer表了。