elasticsearch index 之 Mapping

时间 2019-11-20

标签 elasticsearch index mapping 栏目日志分析繁體版

原文原文链接

Lucene索引的一个特色就filed，索引以field组合。这一特色为索引和搜索提供了很大的灵活性。elasticsearch则在Lucene的基础上更近一步，它能够是 no scheme。实现这一功能的秘密就Mapping。Mapping是对索引各个字段的一种预设，包括索引与分词方式，是否存储等，数据根据字段名在Mapping中找到对应的配置，创建索引。这里将对Mapping的实现结构简单分析，Mapping的放置、更新、应用会在后面的索引fenx中进行说明。app

首先看一下Mapping的实现关系结构，以下图所示：elasticsearch

这只是Mapping中的一部份内容。Mapping扩展了lucene的filed，定义了更多的field类型既有Lucene所拥有的string，number等字段又有date，IP，byte及geo的相关字段，这也是es的强大之处。如上图所示，能够分为两类，mapper与documentmapper，前者是全部mapper的父接口。而DocumentMapper则是Mapper的集合，它表明了一个索引的mapper定义。ide

Mapper的有三类，第一类就是核心field结构FileMapper—>AbstractFieldMapper—>StringField这种核心数据类型，它表明了一类数据类型，如字符串类型，int类型这种；第二类是Mapper—>ObjectMapper—>RootObjectMapper,object类型的Mapper，这也是elasticsearch对lucene的一大改进，不想lucene之支持基本数据类型；最后一类是Mapper—>RootMapper—>IndexFieldMapper这种类型，只存在于根Mapper中的一种Mapper，如IdFieldMapper及图上的IndexFieldMapper，它们相似于index的元数据，只可能存在于某个index内部。post

Mapper中一个比较重要的方法就是parse(ParseContext context)，Mapper的子类对这个方法都有各自的实现。它的主要功能是经过解析ParseContext获取到对应的field，这个方法主要用于创建索引时。索引数据被继续成parsecontext，每一个field解析parseContext构建对应的lucene Field。它在AbstractFieldMapper中的实现以下所示：ui

    public void parse(ParseContext context) throws IOException {
        final List<Field> fields = new ArrayList<>(2);
        try {
            parseCreateField(context, fields);//实际Filed解析方法
            for (Field field : fields) {
                if (!customBoost()) {//设置boost
                    field.setBoost(boost);
                }
                if (context.listener().beforeFieldAdded(this, field, context)) {
                    context.doc().add(field);//将解析完成的Field加入到context中
                }
            }
        } catch (Exception e) {
            throw new MapperParsingException("failed to parse [" + names.fullName() + "]", e);
        }
        multiFields.parse(this, context);//进行mutiFields解析，MultiFields做用是对同一个field作不一样的定义，如能够进行不一样分词方式的索引这样便于经过各类方式查询
        if (copyTo != null) {
            copyTo.parse(context);
        }
    }

这里的parseCreateField是一个抽象方法，每种数据类型都有本身的实现，如string的实现方式以下所示：this

protected void parseCreateField(ParseContext context, List<Field> fields) throws IOException {
        ValueAndBoost valueAndBoost = parseCreateFieldForString(context, nullValue, boost);//解析成值和boost
        if (valueAndBoost.value() == null) {
            return;
        }
        if (ignoreAbove > 0 && valueAndBoost.value().length() > ignoreAbove) {
            return;
        }
        if (context.includeInAll(includeInAll, this)) {
            context.allEntries().addText(names.fullName(), valueAndBoost.value(), valueAndBoost.boost());
        }

        if (fieldType.indexed() || fieldType.stored()) {//构建LuceneField
            Field field = new Field(names.indexName(), valueAndBoost.value(), fieldType);
            field.setBoost(valueAndBoost.boost());
            fields.add(field);
        }
        if (hasDocValues()) {
            fields.add(new SortedSetDocValuesField(names.indexName(), new BytesRef(valueAndBoost.value())));
        }
        if (fields.isEmpty()) {
            context.ignoredValue(names.indexName(), valueAndBoost.value());
        }
    }

//解析出字段的值和boost
    public static ValueAndBoost parseCreateFieldForString(ParseContext context, String nullValue, float defaultBoost) throws IOException {
        if (context.externalValueSet()) {
            return new ValueAndBoost((String) context.externalValue(), defaultBoost);
        }
        XContentParser parser = context.parser();
        if (parser.currentToken() == XContentParser.Token.VALUE_NULL) {
            return new ValueAndBoost(nullValue, defaultBoost);
        }
        if (parser.currentToken() == XContentParser.Token.START_OBJECT) {
            XContentParser.Token token;
            String currentFieldName = null;
            String value = nullValue;
            float boost = defaultBoost;
            while ((token = parser.nextToken()) != XContentParser.Token.END_OBJECT) {
                if (token == XContentParser.Token.FIELD_NAME) {
                    currentFieldName = parser.currentName();
                } else {
                    if ("value".equals(currentFieldName) || "_value".equals(currentFieldName)) {
                        value = parser.textOrNull();
                    } else if ("boost".equals(currentFieldName) || "_boost".equals(currentFieldName)) {
                        boost = parser.floatValue();
                    } else {
                        throw new ElasticsearchIllegalArgumentException("unknown property [" + currentFieldName + "]");
                    }
                }
            }
            return new ValueAndBoost(value, boost);
        }
        return new ValueAndBoost(parser.textOrNull(), defaultBoost);
    }

以上就是Mapper如何将一个值解析成对应的Field的过程，这里只是简单介绍，后面会有详细分析。spa

DocumentMapper是一个索引全部Mapper的集合，它表述了一个索引全部field的定义，能够说是lucene的Document的定义，同时它还包含如下index的默认值，如index和search时默认分词器。它的部分Field以下所示：code

    private final DocumentMapperParser docMapperParser;

    private volatile ImmutableMap<String, Object> meta;

    private volatile CompressedString mappingSource;

    private final RootObjectMapper rootObjectMapper;

    private final ImmutableMap<Class<? extends RootMapper>, RootMapper> rootMappers;
    private final RootMapper[] rootMappersOrdered;
    private final RootMapper[] rootMappersNotIncludedInObject;

    private final NamedAnalyzer indexAnalyzer;

    private final NamedAnalyzer searchAnalyzer;
    private final NamedAnalyzer searchQuoteAnalyzer;

DocumentMapper的功能也体如今parse方法上，它的做用是解析整条数据。以前在Mapper中看到了Field是如何解析出来的，那实际上是在DocumentMapper解析以后。index请求发过来的整条数据在这里被解析出Field，查找Mapping中对应的Field设置，交给它去解析。若是没有且运行动态添加，es则会根据值自动建立一个Field同时更新Mapping。方法代码以下所示：orm

    public ParsedDocument parse(SourceToParse source, @Nullable ParseListener listener) throws MapperParsingException {
        ParseContext.InternalParseContext context = cache.get();

        if (source.type() != null && !source.type().equals(this.type)) {
            throw new MapperParsingException("Type mismatch, provide type [" + source.type() + "] but mapper is of type [" + this.type + "]");
        }
        source.type(this.type);

        XContentParser parser = source.parser();
        try {
            if (parser == null) {
                parser = XContentHelper.createParser(source.source());
            }
            if (sourceTransforms != null) {
                parser = transform(parser);
            }
            context.reset(parser, new ParseContext.Document(), source, listener);

            // will result in START_OBJECT
            int countDownTokens = 0;
            XContentParser.Token token = parser.nextToken();
            if (token != XContentParser.Token.START_OBJECT) {
                throw new MapperParsingException("Malformed content, must start with an object");
            }
            boolean emptyDoc = false;
            token = parser.nextToken();
            if (token == XContentParser.Token.END_OBJECT) {
                // empty doc, we can handle it...
                emptyDoc = true;
            } else if (token != XContentParser.Token.FIELD_NAME) {
                throw new MapperParsingException("Malformed content, after first object, either the type field or the actual properties should exist");
            }
            // first field is the same as the type, this might be because the
            // type is provided, and the object exists within it or because
            // there is a valid field that by chance is named as the type.
            // Because of this, by default wrapping a document in a type is
            // disabled, but can be enabled by setting
            // index.mapping.allow_type_wrapper to true
            if (type.equals(parser.currentName()) && indexSettings.getAsBoolean(ALLOW_TYPE_WRAPPER, false)) {
                parser.nextToken();
                countDownTokens++;
            }

            for (RootMapper rootMapper : rootMappersOrdered) {
                rootMapper.preParse(context);
            }

            if (!emptyDoc) {
                rootObjectMapper.parse(context);
            }

            for (int i = 0; i < countDownTokens; i++) {
                parser.nextToken();
            }

            for (RootMapper rootMapper : rootMappersOrdered) {
                rootMapper.postParse(context);
            }
        } catch (Throwable e) {
            // if its already a mapper parsing exception, no need to wrap it...
            if (e instanceof MapperParsingException) {
                throw (MapperParsingException) e;
            }

            // Throw a more meaningful message if the document is empty.
            if (source.source() != null && source.source().length() == 0) {
                throw new MapperParsingException("failed to parse, document is empty");
            }

            throw new MapperParsingException("failed to parse", e);
        } finally {
            // only close the parser when its not provided externally
            if (source.parser() == null && parser != null) {
                parser.close();
            }
        }
        // reverse the order of docs for nested docs support, parent should be last
        if (context.docs().size() > 1) {
            Collections.reverse(context.docs());
        }
        // apply doc boost
        if (context.docBoost() != 1.0f) {
            Set<String> encounteredFields = Sets.newHashSet();
            for (ParseContext.Document doc : context.docs()) {
                encounteredFields.clear();
                for (IndexableField field : doc) {
                    if (field.fieldType().indexed() && !field.fieldType().omitNorms()) {
                        if (!encounteredFields.contains(field.name())) {
                            ((Field) field).setBoost(context.docBoost() * field.boost());
                            encounteredFields.add(field.name());
                        }
                    }
                }
            }
        }

        ParsedDocument doc = new ParsedDocument(context.uid(), context.version(), context.id(), context.type(), source.routing(), source.timestamp(), source.ttl(), context.docs(), context.analyzer(),
                context.source(), context.mappingsModified()).parent(source.parent());
        // reset the context to free up memory
        context.reset(null, null, null, null);
        return doc;
    }

将整条数据解析成ParsedDocument，解析后的数据才能进行后面的Field解析创建索引。blog

总结：以上就是Mapping的结构和相关功能归纳，Mapper赋予了elasticsearch索引的更强大功能，使得索引和搜索能够支持更多数据类型，灵活性更高。