diff --git a/CHANGELOG.md b/CHANGELOG.md index 6b07b68ab..106af28b9 100755 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -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） diff --git a/hutool-core/src/main/java/cn/hutool/core/lang/Snowflake.java b/hutool-core/src/main/java/cn/hutool/core/lang/Snowflake.java index d22f5c90f..84ae4e7c3 100755 --- a/hutool-core/src/main/java/cn/hutool/core/lang/Snowflake.java +++ b/hutool-core/src/main/java/cn/hutool/core/lang/Snowflake.java @@ -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 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 getIdScopeByTimestamp(long timestampStart, long timestampEnd, boolean ignoreCenterAndWorker) { long startTimeMinId = (timestampStart - twepoch) << TIMESTAMP_LEFT_SHIFT; diff --git a/hutool-core/src/test/java/cn/hutool/core/lang/SnowflakeTest.java b/hutool-core/src/test/java/cn/hutool/core/lang/SnowflakeTest.java index be7a9a76b..30af07072 100755 --- a/hutool-core/src/test/java/cn/hutool/core/lang/SnowflakeTest.java +++ b/hutool-core/src/test/java/cn/hutool/core/lang/SnowflakeTest.java @@ -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 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 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 hashSet = new HashSet<>(); + final HashSet 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 ids = new ConcurrentHashSet<>(); + final Set 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 ids = new ConcurrentHashSet<>(); + final Set ids = new ConcurrentHashSet<>(); ThreadUtil.concurrencyTest(100, () -> { for (int i = 0; i < 50000; i++) { if(false == ids.add(snowflake.nextId())){