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NEO4J亿级数据全文索引构建优化

作者：互联网

NEO4J亿级数据全文索引构建优化

如果使用基于NEO4J的全文检索作为图谱的主要入口，那么做好图谱搜索引擎的优化是非常关键的。

一、数据量规模（亿级）

count(relationships):500584016

count(nodes):765485810

二、构建索引的方式

使用脚本后服务器台执行构建全文索引的操作。
使用后台脚本执行构建索引程序：

index.sh
#!/usr/bin/env bash
nohup /neo4j-community-3.4.9/bin/neo4j-shell -file build.cql >>indexGraph.log 2>&1 &

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build.cql
CALL zdr.index.addChineseFulltextIndex('IKAnalyzer', ['description','fullname','name','lnkurl'], 'LinkedinID') YIELD message RETURN message;

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三、构建索引发生的异常

ERROR (-v for expanded information):
    TransactionFailureException: The database has encountered a critical error, and needs to be restarted. Please see database logs for more details.

 -host      Domain name or IP of host to connect to (default: localhost)
 -port      Port of host to connect to (default: 1337)
 -name      RMI name, i.e. rmi://<host>:<port>/<name> (default: shell)
 -pid       Process ID to connect to
 -c         Command line to execute. After executing it the shell exits
 -file      File containing commands to execute, or '-' to read from stdin. After executing it the shell exits
 -readonly  Connect in readonly mode (only for connecting with -path)
 -path      Points to a neo4j db path so that a local server can be started there
 -config    Points to a config file when starting a local server

Example arguments for remote:
    -port 1337
    -host 192.168.1.234 -port 1337 -name shell
    -host localhost -readonly
    ...or no arguments for default values
Example arguments for local:
    -path /path/to/db
    -path /path/to/db -config /path/to/neo4j.config
    -path /path/to/db -readonly
Caused by: java.lang.OutOfMemoryError: Java heap space | GB+Tree[file:/u02/isi/zdr/graph/neo4j-community-3.4.9/data/databases/graph.db/schema/index/lucene_native-2.0/134/string-1.0/index-134, layout:StringLayout[version:0.1, identifier:24016946018123776], generation:16587/16588]
        at org.neo4j.io.pagecache.impl.muninn.CursorFactory.takeWriteCursor(CursorFactory.java:62)
        at org.neo4j.io.pagecache.impl.muninn.MuninnPagedFile.io(MuninnPagedFile.java:186)
        at org.neo4j.index.internal.gbptree.FreeListIdProvider.releaseId(FreeListIdProvider.java:217)
        at org.neo4j.index.internal.gbptree.InternalTreeLogic.createSuccessorIfNeeded(InternalTreeLogic.java:1289)
        at org.neo4j.index.internal.gbptree.InternalTreeLogic.insertInLeaf(InternalTreeLogic.java:513)
        at org.neo4j.index.internal.gbptree.InternalTreeLogic.insert(InternalTreeLogic.java:356)
        at org.neo4j.index.internal.gbptree.GBPTree$SingleWriter.merge(GBPTree.java:1234)
        at org.neo4j.kernel.impl.index.schema.NativeSchemaIndexUpdater.processAdd(NativeSchemaIndexUpdater.java:132)
        at org.neo4j.kernel.impl.index.schema.NativeSchemaIndexUpdater.processUpdate(NativeSchemaIndexUpdater.java:86)
        at org.neo4j.kernel.impl.index.schema.NativeSchemaIndexUpdater.process(NativeSchemaIndexUpdater.java:61)
        at org.neo4j.kernel.impl.index.schema.fusion.FusionIndexUpdater.process(FusionIndexUpdater.java:41)
        at org.neo4j.kernel.impl.api.index.updater.DelegatingIndexUpdater.process(DelegatingIndexUpdater.java:40)
        at org.neo4j.kernel.impl.api.index.IndexingService.processUpdate(IndexingService.java:516)
        at org.neo4j.kernel.impl.api.index.IndexingService.apply(IndexingService.java:479)
        at org.neo4j.kernel.impl.api.index.IndexingService.apply(IndexingService.java:463)
        at org.neo4j.kernel.impl.transaction.command.IndexUpdatesWork.apply(IndexUpdatesWork.java:63)
        at org.neo4j.kernel.impl.transaction.command.IndexUpdatesWork.apply(IndexUpdatesWork.java:42)
        at org.neo4j.concurrent.WorkSync.doSynchronizedWork(WorkSync.java:231)
        at org.neo4j.concurrent.WorkSync.tryDoWork(WorkSync.java:157)
        at org.neo4j.concurrent.WorkSync.apply(WorkSync.java:91)

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JAVA代码实现索引

    /**
     * @param
     * @return
     * @Description: TODO(构建索引并返回MESSAGE - 不支持自动更新)
     */
    private String chineseFulltextIndex(String indexName, String labelName, List<String> propKeys) {

        Label label = Label.label(labelName);

        // 按照标签找到该标签下的所有节点
        ResourceIterator<Node> nodes = db.findNodes(label);
        System.out.println("nodes:" + nodes.toString());

        int nodesSize = 0;
        int propertiesSize = 0;

        // 循环存在问题 更新到3000万之后程序开始卡顿
        while (nodes.hasNext()) {
            nodesSize++;
            Node node = nodes.next();
            System.out.println("current nodes:" + node.toString());

            // 每个节点上需要添加索引的属性
            Set<Map.Entry<String, Object>> properties = node.getProperties(propKeys.toArray(new String[0])).entrySet();
            System.out.println("current node properties" + properties);

            // 查询该节点是否已有索引，有的话删除
            if (db.index().existsForNodes(indexName)) {
                Index<Node> oldIndex = db.index().forNodes(indexName);
                System.out.println("current node index" + oldIndex);
                oldIndex.remove(node);
            }

            // 为该节点的每个需要添加索引的属性添加全文索引
            Index<Node> nodeIndex = db.index().forNodes(indexName, FULL_INDEX_CONFIG);
            for (Map.Entry<String, Object> property : properties) {
                propertiesSize++;
                nodeIndex.add(node, property.getKey(), property.getValue());
            }
            // 计算耗时
        }

        String message = "IndexName:" + indexName + ",LabelName:" + labelName + ",NodesSize:" + nodesSize + ",PropertiesSize:" + propertiesSize;
        return message;
    }

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四、全文索引代码优化

1、Java.lang.OutOfMemoryError

Java.lang.OutOfMemory是java.lang.VirtualMachineError的一个子类，当Java虚拟机中断，或是超出可用资源时抛出。

2、访问数据库时

访问数据库时程序会获取锁和内存，在事务没有被完成之前锁和内存是不会释放的。因此现在很容易理解上述BUG的出现的原因。（三）实现的索引程序中，是获取节点之后在WHILE循环中执行构建索引，直到索引构建完毕事务才会自动被关闭，自动执行内存回收等操作。当获取的数据量巨大的时候，必然会出现内存溢出。

3、优化方案

使用批量事务提交的机制。

4、优化代码

 /**
     * @param
     * @return
     * @Description: TODO(构建索引并返回MESSAGE - 不支持自动更新)
     */
    private String chineseFulltextIndex(String indexName, String labelName, List<String> propKeys) {

        Label label = Label.label(labelName);

        int nodesSize = 0;
        int propertiesSize = 0;

        // 按照标签找到该标签下的所有节点
        ResourceIterator<Node> nodes = db.findNodes(label);
        Transaction tx = db.beginTx();
        try {
            int batch = 0;
            long startTime = System.nanoTime();
            while (nodes.hasNext()) {
                nodesSize++;
                Node node = nodes.next();

                boolean indexed = false;
                // 每个节点上需要添加索引的属性
                Set<Map.Entry<String, Object>> properties = node.getProperties(propKeys.toArray(new String[0])).entrySet();

                // 查询该节点是否已有索引，有的话删除
                if (db.index().existsForNodes(indexName)) {
                    Index<Node> oldIndex = db.index().forNodes(indexName);
                    oldIndex.remove(node);
                }

                // 为该节点的每个需要添加索引的属性添加全文索引
                Index<Node> nodeIndex = db.index().forNodes(indexName, FULL_INDEX_CONFIG);
                for (Map.Entry<String, Object> property : properties) {
                    indexed = true;
                    propertiesSize++;
                    nodeIndex.add(node, property.getKey(), property.getValue());
                }
                // 批量提交事务
                if (indexed) {
                    if (++batch == 50_000) {
                        batch = 0;
                        tx.success();
                        tx.close();
                        tx = db.beginTx();

                        // 计算耗时
                        startTime = indexConsumeTime(startTime, nodesSize, propertiesSize);
                    }
                }
            }
            tx.success();
            // 计算耗时
            indexConsumeTime(startTime, nodesSize, propertiesSize);
        } finally {
            tx.close();
        }

        String message = "IndexName:" + indexName + ",LabelName:" + labelName + ",NodesSize:" + nodesSize + ",PropertiesSize:" + propertiesSize;
        return message;
    }

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5、执行效率测试

50_000为批次进行提交，依次累加nodeSize和propertieSize，consume还是每批提交的耗时。
可以看到在刚开始提交的时候耗时较多，之后基本上稳定在每批提交耗时：2s~5s/5万条。10亿nodes，耗时估算11h~23h之间。

Build index-nodeSize:50000,propertieSize:148777,consume:21434ms
Build index-nodeSize:100000,propertieSize:297883,consume:18493ms
Build index-nodeSize:150000,propertieSize:446936,consume:17140ms
Build index-nodeSize:200000,propertieSize:595981,consume:17323ms
Build index-nodeSize:250000,propertieSize:745039,consume:19680ms
Build index-nodeSize:300000,propertieSize:894026,consume:18451ms
Build index-nodeSize:350000,propertieSize:1042994,consume:20266ms
Build index-nodeSize:400000,propertieSize:1160186,consume:12787ms
Build index-nodeSize:450000,propertieSize:1210186,consume:1946ms
Build index-nodeSize:500000,propertieSize:1260186,consume:3174ms
Build index-nodeSize:550000,propertieSize:1310186,consume:3090ms
Build index-nodeSize:600000,propertieSize:1360186,consume:3063ms
Build index-nodeSize:650000,propertieSize:1410186,consume:1868ms
Build index-nodeSize:700000,propertieSize:1460186,consume:2036ms
Build index-nodeSize:750000,propertieSize:1510186,consume:3784ms
Build index-nodeSize:800000,propertieSize:1560186,consume:3037ms
Build index-nodeSize:850000,propertieSize:1610186,consume:2627ms
Build index-nodeSize:900000,propertieSize:1660186,consume:1900ms
Build index-nodeSize:950000,propertieSize:1710186,consume:2944ms
Build index-nodeSize:1000000,propertieSize:1760186,consume:3369ms
Build index-nodeSize:1050000,propertieSize:1810186,consume:3289ms
Build index-nodeSize:1100000,propertieSize:1860186,consume:2763ms
Build index-nodeSize:1150000,propertieSize:1910186,consume:3237ms
Build index-nodeSize:1200000,propertieSize:1960186,consume:3408ms
Build index-nodeSize:1250000,propertieSize:2010186,consume:3644ms
Build index-nodeSize:1300000,propertieSize:2060186,consume:3661ms
Build index-nodeSize:1350000,propertieSize:2110186,consume:2964ms
Build index-nodeSize:1400000,propertieSize:2160186,consume:3219ms
Build index-nodeSize:1450000,propertieSize:2210186,consume:3356ms
Build index-nodeSize:1500000,propertieSize:2260186,consume:4115ms
Build index-nodeSize:1550000,propertieSize:2310186,consume:3188ms
Build index-nodeSize:1600000,propertieSize:2360186,consume:3364ms
Build index-nodeSize:1650000,propertieSize:2410186,consume:3799ms
Build index-nodeSize:1700000,propertieSize:2460186,consume:4301ms
Build index-nodeSize:1750000,propertieSize:2510186,consume:3772ms
Build index-nodeSize:1800000,propertieSize:2560186,consume:3692ms
Build index-nodeSize:1850000,propertieSize:2610186,consume:3428ms
Build index-nodeSize:1900000,propertieSize:2660186,consume:2930ms

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备注：在本次测试的数据集上执行索引构建2小时之后，此时已经被索引了1495万个NODES，速度下降明显，需要进一步优化。

Build index-nodeSize:13850000,propertieSize:14610186,consume:97290ms
Build index-nodeSize:13900000,propertieSize:14660186,consume:7441ms
Build index-nodeSize:13950000,propertieSize:14710186,consume:3730ms
Build index-nodeSize:14000000,propertieSize:14760186,consume:3512ms
Build index-nodeSize:14050000,propertieSize:14810186,consume:4545ms
Build index-nodeSize:14100000,propertieSize:14860186,consume:12100ms
Build index-nodeSize:14150000,propertieSize:14910186,consume:83071ms
Build index-nodeSize:14200000,propertieSize:14960186,consume:7417ms
Build index-nodeSize:14250000,propertieSize:15010186,consume:3579ms
Build index-nodeSize:14300000,propertieSize:15060186,consume:64841ms
Build index-nodeSize:14350000,propertieSize:15110186,consume:7553ms
Build index-nodeSize:14400000,propertieSize:15160186,consume:63141ms
Build index-nodeSize:14450000,propertieSize:15210186,consume:64316ms
Build index-nodeSize:14500000,propertieSize:15260186,consume:187510ms
Build index-nodeSize:14550000,propertieSize:15310186,consume:247571ms
Build index-nodeSize:14600000,propertieSize:15360186,consume:224611ms
Build index-nodeSize:14650000,propertieSize:15410186,consume:244539ms
Build index-nodeSize:14700000,propertieSize:15460186,consume:354684ms
Build index-nodeSize:14750000,propertieSize:15510186,consume:236970ms
Build index-nodeSize:14800000,propertieSize:15560186,consume:308532ms
Build index-nodeSize:14850000,propertieSize:15610186,consume:429815ms
Build index-nodeSize:14900000,propertieSize:15660186,consume:409451ms
Build index-nodeSize:14950000,propertieSize:15710186,consume:456980ms

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构建程序在运行4个小时之后，被索引了1530万NODES，索引构建速度几乎慢到不可接受，持续优化中…

Build index-nodeSize:14750000,propertieSize:15510186,consume:236970ms
Build index-nodeSize:14800000,propertieSize:15560186,consume:308532ms
Build index-nodeSize:14850000,propertieSize:15610186,consume:429815ms
Build index-nodeSize:14900000,propertieSize:15660186,consume:409451ms
Build index-nodeSize:14950000,propertieSize:15710186,consume:456980ms
Build index-nodeSize:15000000,propertieSize:15760186,consume:447474ms
Build index-nodeSize:15050000,propertieSize:15810186,consume:580270ms
Build index-nodeSize:15100000,propertieSize:15860186,consume:840488ms
Build index-nodeSize:15150000,propertieSize:15910186,consume:573554ms
Build index-nodeSize:15200000,propertieSize:15960186,consume:748670ms
Build index-nodeSize:15250000,propertieSize:16010186,consume:1305363ms
Build index-nodeSize:15300000,propertieSize:16060186,consume:2495139ms

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上述测试案例的源码位置

标签：index,consume,全文索引,propertieSize,Build,neo4j,亿级,NEO4J,nodeSize
来源： https://www.cnblogs.com/jpfss/p/11411214.html