Java如何從Redis中批量讀取數(shù)據(jù)
一.背景概述
本周接到一個(gè)新的需求:從用戶dau日志文件中讀取用戶uid,然后到Redis中獲取對(duì)應(yīng)的用戶數(shù)據(jù)。用戶的uid存儲(chǔ)于login_day_20220913.txt文件,共1億2千多萬(wàn)條數(shù)據(jù),數(shù)量達(dá)1.4G。
要求:盡量在2小時(shí)內(nèi)獲得結(jié)果,在數(shù)據(jù)處理過程中,Redis服務(wù)器QPS盡量低,不超過某個(gè)閾值,不然會(huì)觸發(fā)監(jiān)控報(bào)警。數(shù)據(jù)從Redis從庫(kù)讀取,只提供一個(gè)端口。
二.分析與實(shí)現(xiàn)
由于之前做過相同數(shù)據(jù)量的統(tǒng)計(jì)需求,所以從一開始就確定單線程完成此次數(shù)據(jù)處理也是可以的。實(shí)際上,對(duì)多線程和并發(fā)的使用需要慎之又慎,特別是在業(yè)務(wù)繁忙的系統(tǒng)或環(huán)境下。
接觸Redis的朋友都知道,Redis是支持批量讀取的,其中常用的兩個(gè)方法:mget()和hmget()。
本次處理的數(shù)據(jù)不是哈希結(jié)構(gòu),所以確定使用mget()。
此時(shí),我自然而然地問了同事一個(gè)問題,那就是mget批量處理數(shù)據(jù)的最佳參數(shù)范圍是多少?因?yàn)閙get()接受一個(gè)字符串?dāng)?shù)組參數(shù),也就是說字符串?dāng)?shù)組的長(zhǎng)度最佳為多少?
同事并沒有給我明確的答案,只是說他們?nèi)粘C颗翁幚?0000條,建議我自己可以嘗試一下,于是我打算試試50000條數(shù)據(jù)。
主要代碼如下:
package com.sina.weibo;
import com.sina.weibo.util.FileUtils;
import com.sina.weibo.util.ListUtil;
import org.apache.commons.lang3.time.StopWatch;
import redis.clients.jedis.Jedis;
import java.util.ArrayList;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.concurrent.TimeUnit;
public class Application {
/** dau數(shù)據(jù)讀取路徑 */
private static String dauDataPath = "/data1/sinawap/var/logs/wapcommon/place/user_position/dau/login_day_20220913.txt";
/** 結(jié)果輸出路徑 */
private static String outputPath = "/data1/bingqing5/importcampusdata/output/campus_data.txt";
/** 已處理過的uid數(shù)據(jù)存儲(chǔ)路徑 */
private static String processedUidDataPath = "/data1/bingqing5/importcampusdata/process/processed_uid.txt";
public static void main(String[] args) {
StopWatch stopWatch = new StopWatch();
// 開始時(shí)間
stopWatch.start();
System.out.println("================程序開始===============");
transfer(dauDataPath, processedUidDataPath, outputPath);
System.out.println("================程序結(jié)束===============");
// 結(jié)束時(shí)間
stopWatch.stop();
// 統(tǒng)計(jì)執(zhí)行時(shí)間(秒)
System.out.println("執(zhí)行時(shí)長(zhǎng):" + stopWatch.getTime(TimeUnit.SECONDS) + " 秒.");
}
private static void transfer(String dauDataPath, String processedUidDataPath, String outputPath) {
List<String> dauDataList = FileUtils.readInfoFromFile(dauDataPath);
List<List<String>> bucket = ListUtil.splitList(dauDataList, 50000);
Jedis jedis = new Jedis("rdsxxxxx.xxxx.xxxx.xxxx.com.cn",50000);
List<String> processedUidDataList = FileUtils.readInfoFromFile(processedUidDataPath);
LinkedHashSet<String> linkedHashSet = ListUtil.getLinkedHashSet(processedUidDataList);
for (List<String> list : bucket) {
List<String> jsonStrList = jedis.mget(list.toArray(new String[list.size()]));
for (int i = 0; i < list.size(); i++) {
if (!linkedHashSet.contains(list.get(i))) {
String uid = list.get(i);
FileUtils.appendInfoToFile(processedUidDataPath, uid);
String jsonStr = jsonStrList.get(i);
if (jsonStr == null || jsonStr == "") continue;
String content = uid + "\t" + jsonStr;
FileUtils.appendInfoToFile(outputPath, content);
}
}
System.out.println(list.size());
}
}
}三.發(fā)現(xiàn)問題與屢次改進(jìn)
3.1.QPS過高而且波動(dòng)很大
上述代碼上線后沒多久,就被同事找來(lái),說QPS過高,開始的時(shí)候瞬間達(dá)到近100k,之后穩(wěn)定在70k~100k之間。因?yàn)閾?dān)心影響其他業(yè)務(wù),于是把jar包暫停,著手優(yōu)化。
于是,我多次修改如下代碼:
List<List<String>> bucket = ListUtil.splitList(dauDataList, 50000);
將50000,調(diào)整為10000,5000,1000,500,100等值逐一嘗試。
QPS確實(shí)逐步降下來(lái)了,但是即便是每次處理1000條,QPS也有40K左右。
3.2.程序中斷,拋異常
最終以每批次讀取500條數(shù)據(jù),將代碼上線。但是程序總是中斷報(bào)錯(cuò),拋出異常:

而這時(shí)候已處理的數(shù)據(jù)量達(dá)到幾千萬(wàn)條。
最初懷疑是因?yàn)閖edis對(duì)象沒有調(diào)用close方法,于是修改代碼如下:
package com.sina.weibo;
import com.sina.weibo.util.FileUtils;
import com.sina.weibo.util.ListUtil;
import org.apache.commons.lang3.time.StopWatch;
import redis.clients.jedis.Jedis;
import java.util.ArrayList;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.concurrent.TimeUnit;
public class Application {
/** dau數(shù)據(jù)讀取路徑 */
private static String dauDataPath = "/data1/sinawap/var/logs/wapcommon/place/user_position/dau/login_day_20220913.txt";
/** 結(jié)果輸出路徑 */
private static String outputPath = "/data1/bingqing5/importcampusdata/output/campus_data.txt";
/** 已處理過的uid數(shù)據(jù)存儲(chǔ)路徑 */
private static String processedUidDataPath = "/data1/bingqing5/importcampusdata/process/processed_uid.txt";
public static void main(String[] args) {
StopWatch stopWatch = new StopWatch();
// 開始時(shí)間
stopWatch.start();
System.out.println("================程序開始===============");
transfer(dauDataPath, processedUidDataPath, outputPath);
System.out.println("================程序結(jié)束===============");
// 結(jié)束時(shí)間
stopWatch.stop();
// 統(tǒng)計(jì)執(zhí)行時(shí)間(秒)
System.out.println("執(zhí)行時(shí)長(zhǎng):" + stopWatch.getTime(TimeUnit.SECONDS) + " 秒.");
}
private static void transfer(String dauDataPath, String processedUidDataPath, String outputPath) {
List<String> dauDataList = FileUtils.readInfoFromFile(dauDataPath);
List<List<String>> bucket = ListUtil.splitList(dauDataList, 50000);
List<String> processedUidDataList = FileUtils.readInfoFromFile(processedUidDataPath);
LinkedHashSet<String> linkedHashSet = ListUtil.getLinkedHashSet(processedUidDataList);
for (List<String> list : bucket) {
Jedis jedis = new Jedis(rdsxxxxx.xxxx.xxxx.xxxx.com.cn", 50000);
List<String> jsonStrList = jedis.mget(list.toArray(new String[list.size()]));
for (int i = 0; i < list.size(); i++) {
if (!linkedHashSet.contains(list.get(i))) {
String uid = list.get(i);
FileUtils.appendInfoToFile(processedUidDataPath, uid);
String jsonStr = jsonStrList.get(i);
if (jsonStr == null || jsonStr == "") continue;
String content = uid + "\t" + jsonStr;
FileUtils.appendInfoToFile(outputPath, content);
}
}
jedis.close();
System.out.println(list.size());
}
}
}修改后跑程序依舊沒有任何改善,繼續(xù)修改,代碼如下:
package com.sina.weibo;
import com.sina.weibo.util.FileUtils;
import com.sina.weibo.util.ListUtil;
import org.apache.commons.lang3.time.StopWatch;
import redis.clients.jedis.Jedis;
import java.util.ArrayList;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.concurrent.TimeUnit;
public class A {
/** dau數(shù)據(jù)讀取路徑 */
private static String dauDataPath = "/data1/sinawap/var/logs/wapcommon/place/user_position/dau/login_day_20220913.txt";
/** 結(jié)果輸出路徑 */
private static String outputPath = "/data1/bingqing5/importcampusdata/output/campus_data.txt";
/** 已處理過的uid數(shù)據(jù)存儲(chǔ)路徑 */
private static String processedUidDataPath = "/data1/bingqing5/importcampusdata/process/processed_uid.txt";
public static void main(String[] args) {
StopWatch stopWatch = new StopWatch();
// 開始時(shí)間
stopWatch.start();
System.out.println("================程序開始===============");
transfer(dauDataPath, processedUidDataPath, outputPath);
System.out.println("================程序結(jié)束===============");
// 結(jié)束時(shí)間
stopWatch.stop();
// 統(tǒng)計(jì)執(zhí)行時(shí)間(秒)
System.out.println("執(zhí)行時(shí)長(zhǎng):" + stopWatch.getTime(TimeUnit.SECONDS) + " 秒.");
}
private static void transfer(String dauDataPath, String processedUidDataPath, String outputPath) {
List<String> dauDataList = FileUtils.readInfoFromFile(dauDataPath);
List<List<String>> bucket = ListUtil.splitList(dauDataList, 50000);
List<String> processedUidDataList = FileUtils.readInfoFromFile(processedUidDataPath);
LinkedHashSet<String> linkedHashSet = ListUtil.getLinkedHashSet(processedUidDataList);
for (List<String> list : bucket) {
Jedis jedis = new Jedis("rdsxxxxx.xxxx.xxxx.xxxx.com.cn", 50000);
List<String> jsonStrList = jedis.mget(list.toArray(new String[list.size()]));
for (int i = 0; i < list.size(); i++) {
if (!linkedHashSet.contains(list.get(i))) {
String uid = list.get(i);
FileUtils.appendInfoToFile(processedUidDataPath, uid);
String jsonStr = jsonStrList.get(i);
if (jsonStr == null || jsonStr == "") continue;
String content = uid + "\t" + jsonStr;
FileUtils.appendInfoToFile(outputPath, content);
}
}
try {
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
jedis.close();
}
System.out.println(list.size());
}
}
}上線以后,觀測(cè)發(fā)現(xiàn)QPS區(qū)域穩(wěn)定,但是程序會(huì)空跑,也就是從頭開始將已處理的數(shù)據(jù)也要逐一讀取一次,很多時(shí)候都沒有跑到上次程序處理的地方就已經(jīng)被迫退出。
linkedHashSet本來(lái)是用來(lái)標(biāo)記上次程序運(yùn)行停止的地方,但是似乎并沒有完全發(fā)揮作用。
于是修改代碼,加入一個(gè)新的list集合,用于存放還沒有處理過的數(shù)據(jù),代碼如下:
package com.sina.weibo;
import com.sina.weibo.util.FileUtils;
import com.sina.weibo.util.ListUtil;
import org.apache.commons.lang3.time.StopWatch;
import redis.clients.jedis.Jedis;
import java.util.ArrayList;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.concurrent.TimeUnit;
/**
* @author bingqing5
* @date 2022/09/14 15:00
* @version 1.0
*/
public class Application {
/** dau數(shù)據(jù)讀取路徑 */
private static String dauDataPath = "/data1/sinawap/var/logs/wapcommon/place/user_position/dau/login_day_20220913.txt";
/** 結(jié)果輸出路徑 */
private static String outputPath = "/data1/bingqing5/importcampusdata/output/campus_data.txt";
/** 已處理過的uid數(shù)據(jù)存儲(chǔ)路徑 */
private static String processedUidDataPath = "/data1/bingqing5/importcampusdata/process/processed_uid.txt";
public static void main(String[] args) {
StopWatch stopWatch = new StopWatch();
// 開始時(shí)間
stopWatch.start();
System.out.println("================程序開始===============");
// transfer(dauDataPath, processedUidDataPath, outputPath);
List<String> dauDataList = FileUtils.readInfoFromFile(dauDataPath);
// List<List<String>> bucket = ListUtil.splitList(dauDataList, 50000);
// Jedis jedis = new Jedis("rdsxxxxx.xxxx.xxxx.xxxx.com.cn", 50000);
List<String> processedUidDataList = FileUtils.readInfoFromFile(processedUidDataPath);
LinkedHashSet<String> linkedHashSet = ListUtil.getLinkedHashSet(processedUidDataList);
List<String> uidList = new ArrayList<>();
for (String uid : dauDataList) {
if (linkedHashSet.contains(uid)) {
continue;
} else {
uidList.add(uid);
}
}
List<List<String>> bucket;
if (uidList.size() != 0) {
bucket = ListUtil.splitList(uidList, 10000);
} else {
bucket = new ArrayList<>();
}
for (List<String> list : bucket) {
Jedis jedis = new Jedis("rdsxxxxx.xxxx.xxxx.xxxx.com.cn", 50000);
List<String> jsonStrList = jedis.mget(list.toArray(new String[list.size()]));
for (int i = 0; i < list.size(); i++) {
if (!linkedHashSet.contains(list.get(i))) {
String uid = list.get(i);
FileUtils.appendInfoToFile(processedUidDataPath, uid);
String jsonStr = jsonStrList.get(i);
if (jsonStr == null || jsonStr == "") continue;
String content = uid + "\t" + jsonStr;
FileUtils.appendInfoToFile(outputPath, content);
}
}
try {
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
jedis.close();
}
System.out.println(list.size());
}
System.out.println("================程序結(jié)束===============");
// 結(jié)束時(shí)間
stopWatch.stop();
// 統(tǒng)計(jì)執(zhí)行時(shí)間(秒)
System.out.println("執(zhí)行時(shí)長(zhǎng):" + stopWatch.getTime(TimeUnit.SECONDS) + " 秒.");
}
}終于這次修改后,上線代碼,代碼平穩(wěn)運(yùn)行。
此時(shí)查看QPS,發(fā)現(xiàn)10000的批讀取量,QPS文檔在25K以下,此前同樣的數(shù)據(jù)量,QPS能達(dá)到40K。

3.3.內(nèi)存消耗過大
在上次修改后,程序平穩(wěn)運(yùn)行,期間我查看了機(jī)器狀態(tài),發(fā)現(xiàn)我跑的jar包竟然消耗了32%左右的內(nèi)存,那臺(tái)機(jī)器也不過62G的總內(nèi)存。雖然不缺內(nèi)存資源,但是還是決定趁著程序在跑的期間,回顧一下代碼。
List<List<String>> bucket = ListUtil.splitList(dauDataList, 10000);
上面這行代碼是將所有的用戶uid數(shù)據(jù)按照10000的大小均等分割,每次遍歷,要重復(fù)創(chuàng)建同一類Jedis對(duì)象,也會(huì)消耗大量?jī)?nèi)存。
另外,下面這段程序:
List<String> uidList = new ArrayList<>();
for (String uid : dauDataList) {
if (linkedHashSet.contains(uid)) {
continue;
} else {
uidList.add(uid);
}
}已經(jīng)對(duì)處理過的數(shù)據(jù)做過篩選,在循環(huán)中再次做如下判斷:
if (!linkedHashSet.contains(list.get(i))) {
}也是多次一舉,會(huì)增加耗時(shí)。
綜合以上考慮,我做了修改,代碼如下:
package com.sina.weibo;
import com.sina.weibo.util.FileUtils;
import com.sina.weibo.util.ListUtil;
import org.apache.commons.lang3.time.StopWatch;
import redis.clients.jedis.Jedis;
import java.util.ArrayList;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.concurrent.TimeUnit;
/**
* @author bingqing5
* @date 2022/09/14 15:00
* @version 1.0
*/
public class Application {
/** dau數(shù)據(jù)讀取路徑 */
private static String dauDataPath = "/data1/sinawap/var/logs/wapcommon/place/user_position/dau/login_day_20220913.txt";
/** 結(jié)果輸出路徑 */
// private static String outputPath = "/data1/bingqing5/redis_test/output/campus_data.txt";
private static String outputPath = "/data1/bingqing/redis_test/output/campus_data.txt";
/** 已處理過的uid數(shù)據(jù)存儲(chǔ)路徑 */
// private static String processedUidDataPath = "/data1/bingqing5/redis_test/process/processed_uid.txt";
private static String processedUidDataPath = "/data1/bingqing/redis_test/process/processed_uid.txt";
public static void main(String[] args) {
StopWatch stopWatch = new StopWatch();
// 開始時(shí)間
stopWatch.start();
System.out.println("================程序開始===============");
transfer(dauDataPath, processedUidDataPath, outputPath);
System.out.println("================程序結(jié)束===============");
// 結(jié)束時(shí)間
stopWatch.stop();
// 統(tǒng)計(jì)執(zhí)行時(shí)間(秒)
System.out.println("執(zhí)行時(shí)長(zhǎng):" + stopWatch.getTime(TimeUnit.SECONDS) + " 秒.");
}
private static void transfer(String dauDataPath, String processedUidDataPath, String outputPath) {
List<String> dauDataList = FileUtils.readInfoFromFile(dauDataPath);
Jedis jedis = new Jedis("rdsxxxxx.xxxx.xxxx.xxxx.com.cn", 50000);
List<String> processedUidDataList = FileUtils.readInfoFromFile(processedUidDataPath);
LinkedHashSet<String> linkedHashSet = ListUtil.getLinkedHashSet(processedUidDataList);
List<String> uidList = new ArrayList<>();
for (String uid : dauDataList) {
if (linkedHashSet.contains(uid)) {
continue;
} else {
uidList.add(uid);
}
}
List<List<String>> bucket;
if (uidList.size() != 0) {
bucket = ListUtil.splitList(uidList, 50000);
} else {
bucket = new ArrayList<>();
}
for (List<String> list : bucket) {
List<String> jsonStrList = jedis.mget(list.toArray(new String[list.size()]));
for (int i = 0; i < list.size(); i++) {
String uid = list.get(i);
FileUtils.appendInfoToFile(processedUidDataPath, uid);
String jsonStr = jsonStrList.get(i);
if (jsonStr == null || jsonStr == "") continue;
String content = uid + "\t" + jsonStr;
FileUtils.appendInfoToFile(outputPath, content);
}
try {
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
jedis.close();
}
System.out.println(list.size());
}
}
}修改代碼以后,替換掉原先運(yùn)行的jar包,接著運(yùn)行。發(fā)現(xiàn)內(nèi)存消耗明顯降低,穩(wěn)定占總內(nèi)存的20%。
然后嘗試修改了mget參數(shù)量,修改為50000條,再次運(yùn)行程序發(fā)現(xiàn)QPS穩(wěn)定在40K左右。

總結(jié)
本篇算是筆者剛接觸Redis不久的一篇隨手記。通過本次需求的開發(fā)經(jīng)歷,讓我對(duì)Redis有了直觀的了解,同時(shí)也理解了代碼優(yōu)化在實(shí)際生產(chǎn)工作和開發(fā)中的潛在價(jià)值。
關(guān)于Redis,在快速直接從Redis讀取數(shù)據(jù)的場(chǎng)景中,尤其是數(shù)據(jù)量大的時(shí)候,為了防止QPS過高,最好在處理一批次數(shù)據(jù)后空出一定的時(shí)間間隔,比如可以讓線程暫時(shí)休眠一定時(shí)間間隔,再進(jìn)行下批次讀取和處理。
關(guān)于代碼優(yōu)化,盡量創(chuàng)建可重復(fù)使用的對(duì)象,非必要不添加同類對(duì)象,避免大量創(chuàng)建對(duì)象帶來(lái)的資源消耗,本次經(jīng)歷也算是很鮮明的體會(huì)到這點(diǎn)。
以上為個(gè)人經(jīng)驗(yàn),希望能給大家一個(gè)參考,也希望大家多多支持腳本之家。
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