flume學習（六）：使用hive來分析flume收集的日誌資料
阿新 • • 發佈：2019-01-04
前面已經講過如何將log4j的日誌輸出到指定的hdfs目錄，我們前面的指定目錄為/flume/events。
如果想用hive來分析採集來的日誌，我們可以將/flume/events下面的日誌資料都load到hive中的表當中去。
如果瞭解hive的load data原理的話，還有一種更簡便的方式，可以省去load data這一步，就是直接將sink1.hdfs.path指定為hive表的目錄。
下面我將詳細描述具體的操作步驟。
我們還是從需求驅動來講解，前面我們採集的資料，都是介面的訪問日誌資料，資料格式是JSON格式如下：
{"requestTime":1405651379758,"requestParams":{"timestamp":1405651377211,"phone":"02038824941","cardName":"測試商家名稱","provinceCode":"440000","cityCode":"440106"},"requestUrl":"/reporter-api/reporter/reporter12/init.do"}
現在有一個需求，我們要統計介面的總呼叫量。
我第一想法就是，hive中建一張表：test 然後將hdfs.path指定為tier1.sinks.sink1.hdfs.path=hdfs://master68:8020/user/hive/warehouse/besttone.db/test
然後select count(*) from test; 完事。
這個方案簡單，粗暴，先這麼幹著。於是會遇到一個問題，我的日誌資料時JSON格式的，需要hive來序列化和反序列化JSON格式的資料到test表的具體欄位當中去。
這有點糟糕，因為hive本身沒有提供JSON的SERDE,但是有提供函式來解析JSON字串，
第一個是（UDF）:
get_json_object(string json_string,string path) 從給定路徑上的JSON字串中抽取出JSON物件，並返回這個物件的JSON字串形式，如果輸入的JSON字串是非法的，則返回NULL。
第二個是表生成函式（UDTF）：json_tuple(string jsonstr,p1,p2,...,pn) 本函式可以接受多個標籤名稱，對輸入的JSON字串進行處理，這個和get_json_object這個UDF類似，不過更高效，其通過一次呼叫就可以獲得多個鍵值，例：select b.* from test_json a lateral view json_tuple(a.id,'id','name') b as f1,f2;通過lateral view行轉列。
最理想的方式就是能有一種JSON SERDE，只要我們LOAD完資料，就直接可以select * from test,而不是select get_json_object這種方式來獲取，N個欄位就要解析N次，效率太低了。
好在cloudrea wiki裡提供了一個json serde類（這個類沒有在發行的hive的jar包中），於是我把它搬來了，如下：
package com.besttone.hive.serde;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hive.serde.serdeConstants;
import org.apache.hadoop.hive.serde2.SerDe;
import org.apache.hadoop.hive.serde2.SerDeException;
import org.apache.hadoop.hive.serde2.SerDeStats;
import org.apache.hadoop.hive.serde2.objectinspector.ListObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.MapObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.StructField;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.typeinfo.ListTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.MapTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.StructTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.codehaus.jackson.map.ObjectMapper;
/**
* This SerDe can be used for processing JSON data in Hive. It supports
* arbitrary JSON data, and can handle all Hive types except for UNION. However,
* the JSON data is expected to be a series of discrete records, rather than a
* JSON array of objects.
*
* The Hive table is expected to contain columns with names corresponding to
* fields in the JSON data, but it is not necessary for every JSON field to have
* a corresponding Hive column. Those JSON fields will be ignored during
* queries.
*
* Example:
*
* { "a": 1, "b": [ "str1", "str2" ], "c": { "field1": "val1" } }
*
* Could correspond to a table:
*
* CREATE TABLE foo (a INT, b ARRAY<STRING>, c STRUCT<field1:STRING>);
*
* JSON objects can also interpreted as a Hive MAP type, so long as the keys and
* values in the JSON object are all of the appropriate types. For example, in
* the JSON above, another valid table declaraction would be:
*
* CREATE TABLE foo (a INT, b ARRAY<STRING>, c MAP<STRING,STRING>);
*
* Only STRING keys are supported for Hive MAPs.
*/
public class JSONSerDe implements SerDe {
private StructTypeInfo rowTypeInfo;
private ObjectInspector rowOI;
private List<String> colNames;
private List<Object> row = new ArrayList<Object>();
//遇到非JSON格式輸入的時候的處理。
private boolean ignoreInvalidInput;
/**
* An initialization function used to gather information about the table.
* Typically, a SerDe implementation will be interested in the list of
* column names and their types. That information will be used to help
* perform actual serialization and deserialization of data.
*/
@Override
public void initialize(Configuration conf, Properties tbl)
throws SerDeException {
// 遇到無法轉換成JSON物件的字串時，是否忽略，預設不忽略，丟擲異常，設定為true將跳過異常。
ignoreInvalidInput = Boolean.valueOf(tbl.getProperty(
"input.invalid.ignore", "false"));
// Get a list of the table's column names.
String colNamesStr = tbl.getProperty(serdeConstants.LIST_COLUMNS);
colNames = Arrays.asList(colNamesStr.split(","));
// Get a list of TypeInfos for the columns. This list lines up with
// the list of column names.
String colTypesStr = tbl.getProperty(serdeConstants.LIST_COLUMN_TYPES);
List<TypeInfo> colTypes = TypeInfoUtils
.getTypeInfosFromTypeString(colTypesStr);
rowTypeInfo = (StructTypeInfo) TypeInfoFactory.getStructTypeInfo(
colNames, colTypes);
rowOI = TypeInfoUtils
.getStandardJavaObjectInspectorFromTypeInfo(rowTypeInfo);
}
/**
* This method does the work of deserializing a record into Java objects
* that Hive can work with via the ObjectInspector interface. For this
* SerDe, the blob that is passed in is a JSON string, and the Jackson JSON
* parser is being used to translate the string into Java objects.
*
* The JSON deserialization works by taking the column names in the Hive
* table, and looking up those fields in the parsed JSON object. If the
* value of the field is not a primitive, the object is parsed further.
*/
@Override
public Object deserialize(Writable blob) throws SerDeException {
Map<?, ?> root = null;
row.clear();
try {
ObjectMapper mapper = new ObjectMapper();
// This is really a Map<String, Object>. For more information about
// how
// Jackson parses JSON in this example, see
// http://wiki.fasterxml.com/JacksonDataBinding
root = mapper.readValue(blob.toString(), Map.class);
} catch (Exception e) {
// 如果為true,不丟擲異常，忽略該行資料
if (!ignoreInvalidInput)
throw new SerDeException(e);
else {
return null;
}
}
// Lowercase the keys as expected by hive
Map<String, Object> lowerRoot = new HashMap();
for (Map.Entry entry : root.entrySet()) {
lowerRoot.put(((String) entry.getKey()).toLowerCase(),
entry.getValue());
}
root = lowerRoot;
Object value = null;
for (String fieldName : rowTypeInfo.getAllStructFieldNames()) {
try {
TypeInfo fieldTypeInfo = rowTypeInfo
.getStructFieldTypeInfo(fieldName);
value = parseField(root.get(fieldName), fieldTypeInfo);
} catch (Exception e) {
value = null;
}
row.add(value);
}
return row;
}
/**
* Parses a JSON object according to the Hive column's type.
*
* @param field
* - The JSON object to parse
* @param fieldTypeInfo
* - Metadata about the Hive column
* @return - The parsed value of the field
*/
private Object parseField(Object field, TypeInfo fieldTypeInfo) {
switch (fieldTypeInfo.getCategory()) {
case PRIMITIVE:
// Jackson will return the right thing in this case, so just return
// the object
if (field instanceof String) {
field = field.toString().replaceAll("\n", "\\\\n");
}
return field;
case LIST:
return parseList(field, (ListTypeInfo) fieldTypeInfo);
case MAP:
return parseMap(field, (MapTypeInfo) fieldTypeInfo);
case STRUCT:
return parseStruct(field, (StructTypeInfo) fieldTypeInfo);
case UNION:
// Unsupported by JSON
default:
return null;
}
}
/**
* Parses a JSON object and its fields. The Hive metadata is used to
* determine how to parse the object fields.
*
* @param field
* - The JSON object to parse
* @param fieldTypeInfo
* - Metadata about the Hive column
* @return - A map representing the object and its fields
*/
private Object parseStruct(Object field, StructTypeInfo fieldTypeInfo) {
Map<Object, Object> map = (Map<Object, Object>) field;
ArrayList<TypeInfo> structTypes = fieldTypeInfo
.getAllStructFieldTypeInfos();
ArrayList<String> structNames = fieldTypeInfo.getAllStructFieldNames();
List<Object> structRow = new ArrayList<Object>(structTypes.size());
for (int i = 0; i < structNames.size(); i++) {
structRow.add(parseField(map.get(structNames.get(i)),
structTypes.get(i)));
}
return structRow;
}
/**
* Parse a JSON list and its elements. This uses the Hive metadata for the
* list elements to determine how to parse the elements.
*
* @param field
* - The JSON list to parse
* @param fieldTypeInfo
* - Metadata about the Hive column
* @return - A list of the parsed elements
*/
private Object parseList(Object field, ListTypeInfo fieldTypeInfo) {
ArrayList<Object> list = (ArrayList<Object>) field;
TypeInfo elemTypeInfo = fieldTypeInfo.getListElementTypeInfo();
for (int i = 0; i < list.size(); i++) {
list.set(i, parseField(list.get(i), elemTypeInfo));
}
return list.toArray();
}
/**
* Parse a JSON object as a map. This uses the Hive metadata for the map
* values to determine how to parse the values. The map is assumed to have a
* string for a key.
*
* @param field
* - The JSON list to parse
* @param fieldTypeInfo
* - Metadata about the Hive column
* @return
*/
private Object parseMap(Object field, MapTypeInfo fieldTypeInfo) {
Map<Object, Object> map = (Map<Object, Object>) field;
TypeInfo valueTypeInfo = fieldTypeInfo.getMapValueTypeInfo();
for (Map.Entry<Object, Object> entry : map.entrySet()) {
map.put(entry.getKey(), parseField(entry.getValue(), valueTypeInfo));
}
return map;
}
/**
* Return an ObjectInspector for the row of data
*/
@Override
public ObjectInspector getObjectInspector() throws SerDeException {
return rowOI;
}
/**
* Unimplemented
*/
@Override
public SerDeStats getSerDeStats() {
return null;
}
/**
* JSON is just a textual representation, so our serialized class is just
* Text.
*/
@Override
public Class<? extends Writable> getSerializedClass() {
return Text.class;
}
/**
* This method takes an object representing a row of data from Hive, and
* uses the ObjectInspector to get the data for each column and serialize
* it. This implementation deparses the row into an object that Jackson can
* easily serialize into a JSON blob.
*/
@Override
public Writable serialize(Object obj, ObjectInspector oi)
throws SerDeException {
Object deparsedObj = deparseRow(obj, oi);
ObjectMapper mapper = new ObjectMapper();
try {
// Let Jackson do the work of serializing the object
return new Text(mapper.writeValueAsString(deparsedObj));
} catch (Exception e) {
throw new SerDeException(e);
}
}
/**
* Deparse a Hive object into a Jackson-serializable object. This uses the
* ObjectInspector to extract the column data.
*
* @param obj
* - Hive object to deparse
* @param oi
* - ObjectInspector for the object
* @return - A deparsed object
*/
private Object deparseObject(Object obj, ObjectInspector oi) {
switch (oi.getCategory()) {
case LIST:
return deparseList(obj, (ListObjectInspector) oi);
case MAP:
return deparseMap(obj, (MapObjectInspector) oi);
case PRIMITIVE:
return deparsePrimitive(obj, (PrimitiveObjectInspector) oi);
case STRUCT:
return deparseStruct(obj, (StructObjectInspector) oi, false);
case UNION:
// Unsupported by JSON
default:
return null;
}
}
/**
* Deparses a row of data. We have to treat this one differently from other
* structs, because the field names for the root object do not match the
* column names for the Hive table.
*
* @param obj
* - Object representing the top-level row
* @param structOI
* - ObjectInspector for the row
* @return - A deparsed row of data
*/
private Object deparseRow(Object obj, ObjectInspector structOI) {
return deparseStruct(obj, (StructObjectInspector) structOI, true);
}
/**
* Deparses struct data into a serializable JSON object.
*
* @param obj
* - Hive struct data
* @param structOI
* - ObjectInspector for the struct
* @param isRow
* - Whether or not this struct represents a top-level row
* @return - A deparsed struct
*/
private Object deparseStruct(Object obj, StructObjectInspector structOI,
boolean isRow) {
Map<Object, Object> struct = new HashMap<Object, Object>();
List<? extends StructField> fields = structOI.getAllStructFieldRefs();
for (int i = 0; i < fields.size(); i++) {
StructField field = fields.get(i);
// The top-level row object is treated slightly differently from
// other
// structs, because the field names for the row do not correctly
// reflect
// the Hive column names. For lower-level structs, we can get the
// field
// name from the associated StructField object.
String fieldName = isRow ? colNames.get(i) : field.getFieldName();
ObjectInspector fieldOI = field.getFieldObjectInspector();
Object fieldObj = structOI.getStructFieldData(obj, field);
struct.put(fieldName, deparseObject(fieldObj, fieldOI));
}
return struct;
}
/**
* Deparses a primitive type.
*
* @param obj
* - Hive object to deparse
* @param oi
* - ObjectInspector for the object
* @return - A deparsed object
*/
private Object deparsePrimitive(Object obj, PrimitiveObjectInspector primOI) {
return primOI.getPrimitiveJavaObject(obj);
}
private Object deparseMap(Object obj, MapObjectInspector mapOI) {
Map<Object, Object> map = new HashMap<Object, Object>();
ObjectInspector mapValOI = mapOI.getMapValueObjectInspector();
Map<?, ?> fields = mapOI.getMap(obj);
for (Map.Entry<?, ?> field : fields.entrySet()) {
Object fieldName = field.getKey();
Object fieldObj = field.getValue();
map.put(fieldName, deparseObject(fieldObj, mapValOI));
}
return map;
}
/**
* Deparses a list and its elements.
*
* @param obj
* - Hive object to deparse
* @param oi
* - ObjectInspector for the object
* @return - A deparsed object
*/
private Object deparseList(Object obj, ListObjectInspector listOI) {
List<Object> list = new ArrayList<Object>();
List<?> field = listOI.getList(obj);
ObjectInspector elemOI = listOI.getListElementObjectInspector();
for (Object elem : field) {
list.add(deparseObject(elem, elemOI));
}
return list;
}
}我稍微修改了一點東西，多加了一個引數input.invalid.ignore,對應的變數為：
//遇到非JSON格式輸入的時候的處理。
private boolean ignoreInvalidInput;
在deserialize方法中原來是如果傳入的是非JSON格式字串的話，直接丟擲了SerDeException，我加了一個引數來控制它是否丟擲異常，在initialize方法中初始化這個變數（預設為false）：
// 遇到無法轉換成JSON物件的字串時，是否忽略，預設不忽略，丟擲異常，設定為true將跳過異常。
ignoreInvalidInput = Boolean.valueOf(tbl.getProperty(
"input.invalid.ignore", "false"));
好的，現在將這個類打成JAR包： JSONSerDe.jar，放在hive_home的auxlib目錄下（我的是/etc/hive/auxlib），然後修改hive-env.sh，新增HIVE_AUX_JARS_PATH=/etc/hive/auxlib/JSONSerDe.jar，這樣每次執行hive客戶端的時候都會將這個jar包新增到classpath，否則在設定SERDE的時候會報找不到類。
現在我們在HIVE中建立一張表用來存放日誌資料：
create table test(
requestTime BIGINT,
requestParams STRUCT<timestamp:BIGINT,phone:STRING,cardName:STRING,provinceCode:STRING,cityCode:STRING>,
requestUrl STRING)
row format serde "com.besttone.hive.serde.JSONSerDe"
WITH SERDEPROPERTIES(
"input.invalid.ignore"="true",
"requestTime"="$.requestTime",
"requestParams.timestamp"="$.requestParams.timestamp",
"requestParams.phone"="$.requestParams.phone",
"requestParams.cardName"="$.requestParams.cardName",
"requestParams.provinceCode"="$.requestParams.provinceCode",
"requestParams.cityCode"="$.requestParams.cityCode",
"requestUrl"="$.requestUrl");這個表結構就是按照日誌格式設計的，還記得前面說過的日誌資料如下：
{"requestTime":1405651379758,"requestParams":{"timestamp":1405651377211,"phone":"02038824941","cardName":"測試商家名稱","provinceCode":"440000","cityCode":"440106"},"requestUrl":"/reporter-api/reporter/reporter12/init.do"}
我使用了一個STRUCT型別來儲存requestParams的值，row format我們用的是自定義的json serde:com.besttone.hive.serde.JSONSerDe，SERDEPROPERTIES中，除了設定JSON物件的對映關係外，我還設定了一個自定義的引數："input.invalid.ignore"="true"，忽略掉所有非JSON格式的輸入行。這裡不是真正意義的忽略，只是非法行的每個輸出欄位都為NULL了，要在結果集上忽略，必須這樣寫：select * from test where requestUrl is not null;
OK表建好了，現在就差資料了，我們啟動flumedemo的WriteLog,往hive表test目錄下面輸出一些日誌資料，然後在進入hive客戶端，select * from test;所以欄位都正確的解析，大功告成。
flume.conf如下：
tier1.sources=source1
tier1.channels=channel1
tier1.sinks=sink1
tier1.sources.source1.type=avro
tier1.sources.source1.bind=0.0.0.0
tier1.sources.source1.port=44444
tier1.sources.source1.channels=channel1
tier1.sources.source1.interceptors=i1 i2
tier1.sources.source1.interceptors.i1.type=regex_filter
tier1.sources.source1.interceptors.i1.regex=\\{.*\\}
tier1.sources.source1.interceptors.i2.type=timestamp
tier1.channels.channel1.type=memory
tier1.channels.channel1.capacity=10000
tier1.channels.channel1.transactionCapacity=1000
tier1.channels.channel1.keep-alive=30
tier1.sinks.sink1.type=hdfs
tier1.sinks.sink1.channel=channel1
tier1.sinks.sink1.hdfs.path=hdfs://master68:8020/user/hive/warehouse/besttone.db/test
tier1.sinks.sink1.hdfs.fileType=DataStream
tier1.sinks.sink1.hdfs.writeFormat=Text
tier1.sinks.sink1.hdfs.rollInterval=0
tier1.sinks.sink1.hdfs.rollSize=10240
tier1.sinks.sink1.hdfs.rollCount=0
tier1.sinks.sink1.hdfs.idleTimeout=60
besttone.db是我在hive中建立的資料庫，瞭解hive的應該理解沒多大問題。
OK，到這篇文章為止，整個從LOG4J生產日誌，到flume收集日誌，再到用hive離線分析日誌，一整套流水線都講解完了。