【文件属性】：
文件名称：数据立方体datacube.zip
文件大小：4.13MB
文件格式：ZIP
更新时间：2022-08-04 23:47:24
开源项目
数据立方体是复杂计算的抽象。Datacube 是用 Java 实现的，可插入数据库后端支持的数据立方体。
datacube 是用来存储大数据点的聚合信息。数据立方体存储的是有趣输入数据点的子集。比如，你正在编写一个 web 服务器日志分析工具，你的输入点可能是日志行，你可能会计算每个浏览器的类型，每个浏览器的版本，操作系统类型，操作系统版本和其他属性。同时你可能会需要计算一个特定的组合计数(浏览器类型，浏览器版本，操作系统类型)， (浏览器类型，浏览器版本，操作系统类型，操作系统版本)，等等。
这对快速添加和修改计数是个很大的挑战，会浪费很多时间在数据库代码和重新用新计数器处理旧数据。而数据立方体就可以帮忙解决这些问题。
Urban Airship 使用 datacube 项目来支持他们的移动端应用的分析栈，每个节点每秒处理大约 10 K 的事件。
datacube 要求 JDK 1.6。
特性
性能： 高速异步 IO 后端处理
使用 Hadoop MapReduce 进行批量加载
可插入数据库接口
datacube 暂时只支持 HBase 数据库后端。
示例：
IdService idService = new CachingIdService(5, new MapIdService());
ConcurrentMap
backingMap =
new ConcurrentHashMap();
DbHarness dbHarness = new MapDbHarness(backingMap, LongOp.DESERIALIZER,
CommitType.READ_COMBINE_CAS, idService);
HourDayMonthBucketer hourDayMonthBucketer = new HourDayMonthBucketer();
Dimension time = new Dimension("time", hourDayMonthBucketer, false, 8);
Dimension zipcode = new Dimension("zipcode", new StringToBytesBucketer(),
true, 5);
DataCubeIo cubeIo = null;
DataCube cube;
Rollup hourAndZipRollup = new Rollup(zipcode, time, HourDayMonthBucketer.hours);
Rollup dayAndZipRollup = new Rollup(zipcode, time, HourDayMonthBucketer.days);
Rollup hourRollup = new Rollup(time, HourDayMonthBucketer.hours);
Rollup dayRollup = new Rollup(time, HourDayMonthBucketer.days);
List dimensions = ImmutableList.of(time, zipcode);
List rollups = ImmutableList.of(hourAndZipRollup, dayAndZipRollup, hourRollup,
dayRollup);
cube = new DataCube(dimensions, rollups);
cubeIo = new DataCubeIo(cube, dbHarness, 1, Long.MAX_VALUE, SyncLevel.FULL_SYNC);
DateTime now = new DateTime(DateTimeZone.UTC);
// Do an increment of 5 for a certain time and zipcode
cubeIo.writeSync(new LongOp(5), new WriteBuilder(cube)
.at(time, now)
.at(zipcode, "97201"));
// Do an increment of 10 for the same zipcode in a different hour of the same day
DateTime differentHour = now.withHourOfDay((now.getHourOfDay() 1)$);
cubeIo.writeSync(new LongOp(10), new WriteBuilder(cube)
.at(time, differentHour)
.at(zipcode, "97201"));
// Read back the value that we wrote for the current hour, should be 5
Optional thisHourCount = cubeIo.get(new ReadBuilder(cube)
.at(time, HourDayMonthBucketer.hours, now)
.at(zipcode, "97201"));
Assert.assertTrue(thisHourCount.isPresent());
Assert.assertEquals(5L, thisHourCount.get().getLong());
// Read back the value we wrote for the other hour, should be 10
Optional differentHourCount = cubeIo.get(new ReadBuilder(cube)
.at(time, HourDayMonthBucketer.hours, differentHour)
.at(zipcode, "97201"));
Assert.assertTrue(differentHourCount.isPresent());
Assert.assertEquals(10L, differentHourCount.get().getLong());
// The total for today should be the sum of the two increments
Optional todayCount = cubeIo.get(new ReadBuilder(cube)
.at(time, HourDayMonthBucketer.days, now)
.at(zipcode, "97201"));
Assert.assertTrue(todayCount.isPresent());
Assert.assertEquals(15L, todayCount.get().getLong());
标签：datacube
