ZhouXY108/hutool
1
0
Fork 0
mirror of https://gitee.com/chinabugotech/hutool.git synced 2025-05-09 23:51:34 +08:00
Code Issues Packages Projects Releases Wiki Activity

Snowflake增加方法：根据传入时间戳，计算ID起终点

Browse Source
This commit is contained in:
Looly 2023-10-25 11:24:43 +08:00
parent fe37c1aa84
commit bea36c2b5f
3 changed files with 24 additions and 48 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
CHANGELOG.md
View File

@ -9,6 +9,7 @@
* 【core 】 新增ProxySocketFactory
* 【http 】 UserAgent增加百度浏览器识别（issue#I847JY@Gitee）
* 【core 】 ReflectUtil.getFieldsValue增加Filter重载（pr#1090@Gitee）
* 【core 】 Snowflake增加方法：根据传入时间戳，计算ID起终点（pr#1096@Gitee）
### 🐞Bug修复
* 【cron 】 修复Cron表达式range解析错误问题（issue#I82CSH@Gitee）

4
hutool-core/src/main/java/cn/hutool/core/lang/Snowflake.java
View File

@ -204,7 +204,7 @@ public class Snowflake implements Serializable {
* @param timestampStart 开始时间戳
* @param timestampEnd 结束时间戳
* @return key-ID起点，Value-ID终点
* @since 5.8.24
* @since 5.8.23
*/
public Pair<Long, Long> getIdScopeByTimestamp(long timestampStart, long timestampEnd) {
return getIdScopeByTimestamp(timestampStart, timestampEnd, true);
@ -217,7 +217,7 @@ public class Snowflake implements Serializable {
* @param timestampEnd 结束时间戳
* @param ignoreCenterAndWorker 是否忽略数据中心和机器节点的占位，忽略后可获得分布式环境全局可信赖的起终点。
* @return key-ID起点，Value-ID终点
* @since 5.8.24
* @since 5.8.23
*/
public Pair<Long, Long> getIdScopeByTimestamp(long timestampStart, long timestampEnd, boolean ignoreCenterAndWorker) {
long startTimeMinId = (timestampStart - twepoch) << TIMESTAMP_LEFT_SHIFT;

67
hutool-core/src/test/java/cn/hutool/core/lang/SnowflakeTest.java
View File

@ -25,65 +25,40 @@ public class SnowflakeTest {
*/
@Test
public void snowflakeTestGetIdScope() {
long workerId = RandomUtil.randomLong(31);
long dataCenterId = RandomUtil.randomLong(31);
Snowflake idWorker = new Snowflake(workerId, dataCenterId);
long generatedId = idWorker.nextId();
final long workerId = RandomUtil.randomLong(31);
final long dataCenterId = RandomUtil.randomLong(31);
final Snowflake idWorker = new Snowflake(workerId, dataCenterId);
final long generatedId = idWorker.nextId();
// 随机忽略数据中心和工作机器的占位
boolean ignore = RandomUtil.randomBoolean();
long createTimestamp = idWorker.getGenerateDateTime(generatedId);
Pair<Long, Long> idScope = idWorker.getIdScopeByTimestamp(createTimestamp, createTimestamp, ignore);
long startId = idScope.getKey();
long endId = idScope.getValue();
final boolean ignore = RandomUtil.randomBoolean();
final long createTimestamp = idWorker.getGenerateDateTime(generatedId);
final Pair<Long, Long> idScope = idWorker.getIdScopeByTimestamp(createTimestamp, createTimestamp, ignore);
final long startId = idScope.getKey();
final long endId = idScope.getValue();
System.out.println(longToBinaryReadable(generatedId) + " = generatedId longToBinaryReadable");
System.out.println(longToBinaryReadable(startId) + " = startId longToBinaryReadable");
System.out.println(longToBinaryReadable(endId) + " = endId longToBinaryReadable");
// 起点终点相差比较
long trueOffSet = endId - startId;
final long trueOffSet = endId - startId;
// 忽略数据中心和工作机器时差值为22个1，否则为12个1
long expectedOffSet = ignore ? ~(-1 << 22) : ~(-1 << 12);
System.out.println("计算差值 = " + trueOffSet + ", 预期差值 = " + expectedOffSet);
final long expectedOffSet = ignore ? ~(-1 << 22) : ~(-1 << 12);
Assert.assertEquals(trueOffSet, expectedOffSet);
}
/**
* long转雪花格式的2进制字符串
*
* @param number long值
* @return 符号位（1bit）- 时间戳相对值（41bit）- 数据中心标志（5bit）- 机器标志（5bit）- 递增序号（12bit）
*/
private String longToBinaryReadable(long number) {
String binaryString = Long.toBinaryString(number);
StringBuilder sb = new StringBuilder(binaryString);
while (sb.length() < 64) {
sb.insert(0, '0'); // 在二进制字符串前面补零
}
sb
.insert(52, "-")
.insert(47, "-")
.insert(42, "-")
.insert(1, "-")
;
return sb.toString();
}
@Test
public void snowflakeTest1(){
//构建Snowflake，提供终端ID和数据中心ID
Snowflake idWorker = new Snowflake(0, 0);
long nextId = idWorker.nextId();
final Snowflake idWorker = new Snowflake(0, 0);
final long nextId = idWorker.nextId();
Assert.assertTrue(nextId > 0);
}
@Test
public void snowflakeTest(){
HashSet<Long> hashSet = new HashSet<>();
final HashSet<Long> hashSet = new HashSet<>();
//构建Snowflake，提供终端ID和数据中心ID
Snowflake idWorker = new Snowflake(0, 0);
final Snowflake idWorker = new Snowflake(0, 0);
for (int i = 0; i < 1000; i++) {
long id = idWorker.nextId();
final long id = idWorker.nextId();
hashSet.add(id);
}
Assert.assertEquals(1000L, hashSet.size());
@ -92,8 +67,8 @@ public class SnowflakeTest {
@Test
public void snowflakeGetTest(){
//构建Snowflake，提供终端ID和数据中心ID
Snowflake idWorker = new Snowflake(1, 2);
long nextId = idWorker.nextId();
final Snowflake idWorker = new Snowflake(1, 2);
final long nextId = idWorker.nextId();
Assert.assertEquals(1, idWorker.getWorkerId(nextId));
Assert.assertEquals(2, idWorker.getDataCenterId(nextId));
@ -104,9 +79,9 @@ public class SnowflakeTest {
@Ignore
public void uniqueTest(){
// 测试并发环境下生成ID是否重复
Snowflake snowflake = IdUtil.getSnowflake(0, 0);
final Snowflake snowflake = IdUtil.getSnowflake(0, 0);
Set<Long> ids = new ConcurrentHashSet<>();
final Set<Long> ids = new ConcurrentHashSet<>();
ThreadUtil.concurrencyTest(100, () -> {
for (int i = 0; i < 50000; i++) {
if(false == ids.add(snowflake.nextId())){
@ -143,7 +118,7 @@ public class SnowflakeTest {
final Snowflake snowflake = new Snowflake(null, 0, 0,
false, Snowflake.DEFAULT_TIME_OFFSET, 100);
Set<Long> ids = new ConcurrentHashSet<>();
final Set<Long> ids = new ConcurrentHashSet<>();
ThreadUtil.concurrencyTest(100, () -> {
for (int i = 0; i < 50000; i++) {
if(false == ids.add(snowflake.nextId())){