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new cef5769 [HUDI-256] Translate Comparison page (#925)
cef5769 is described below
commit cef57691228de09429cd8794117dee6fc8f729d2
Author: leesf <490081...@qq.com>
AuthorDate: Thu Sep 26 10:00:59 2019 +0800
[HUDI-256] Translate Comparison page (#925)
* [HUDI-256] Translate Comparison page
* [hotfix] address comments
---
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-Apache Hudi fills a big void for processing data on top of DFS, and thus
mostly co-exists nicely with these technologies. However,
-it would be useful to understand how Hudi fits into the current big data
ecosystem, contrasting it with a few related systems
-and bring out the different tradeoffs these systems have accepted in their
design.
+Apache Hudi填补了在DFS上处理数据的巨大空白，并可以和这些技术很好地共存。然而，
+通过将Hudi与一些相关系统进行对比，来了解Hudi如何适应当前的大数据生态系统，并知晓这些系统在设计中做的不同权衡仍将非常有用。
## Kudu
-[Apache Kudu](https://kudu.apache.org) is a storage system that has similar
goals as Hudi, which is to bring real-time analytics on petabytes of data via
first
-class support for `upserts`. A key differentiator is that Kudu also attempts
to serve as a datastore for OLTP workloads, something that Hudi does not aspire
to be.
-Consequently, Kudu does not support incremental pulling (as of early 2017),
something Hudi does to enable incremental processing use cases.
+[Apache
Kudu](https://kudu.apache.org)是一个与Hudi具有相似目标的存储系统，该系统通过对`upserts`支持来对PB级数据进行实时分析。
+一个关键的区别是Kudu还试图充当OLTP工作负载的数据存储，而Hudi并不希望这样做。
+因此，Kudu不支持增量拉取(截至2017年初)，而Hudi支持以便进行增量处理。
+Kudu与分布式文件系统抽象和HDFS完全不同，它自己的一组存储服务器通过RAFT相互通信。
+与之不同的是，Hudi旨在与底层Hadoop兼容的文件系统(HDFS，S3或Ceph)一起使用，并且没有自己的存储服务器群，而是依靠Apache
Spark来完成繁重的工作。
+因此，Hudi可以像其他Spark作业一样轻松扩展，而Kudu则需要硬件和运营支持，特别是HBase或Vertica等数据存储系统。
+到目前为止，我们还没有做任何直接的基准测试来比较Kudu和Hudi(鉴于RTTable正在进行中)。
+但是，如果我们要使用[CERN](https://db-blog.web.cern.ch/blog/zbigniew-baranowski/2017-01-performance-comparison-different-file-formats-and-storage-engines)，
+我们预期Hudi在摄取parquet上有更卓越的性能。
-Kudu diverges from a distributed file system abstraction and HDFS altogether,
with its own set of storage servers talking to each other via RAFT.
-Hudi, on the other hand, is designed to work with an underlying Hadoop
compatible filesystem (HDFS,S3 or Ceph) and does not have its own fleet of
storage servers,
-instead relying on Apache Spark to do the heavy-lifting. Thu, Hudi can be
scaled easily, just like other Spark jobs, while Kudu would require hardware
-& operational support, typical to datastores like HBase or Vertica. We have
not at this point, done any head to head benchmarks against Kudu (given RTTable
is WIP).
-But, if we were to go with results shared by
[CERN](https://db-blog.web.cern.ch/blog/zbigniew-baranowski/2017-01-performance-comparison-different-file-formats-and-storage-engines)
,
-we expect Hudi to positioned at something that ingests parquet with superior
performance.
+## Hive事务
+[Hive事务/ACID](https://cwiki.apache.org/confluence/display/Hive/Hive+Transactions)是另一项类似的工作，它试图实现在ORC文件格式之上的存储`读取时合并`。
+可以理解，此功能与Hive以及[LLAP](https://cwiki.apache.org/confluence/display/Hive/LLAP)之类的其他工作紧密相关。
+Hive事务不提供Hudi提供的读取优化存储选项或增量拉取。
+在实现选择方面，Hudi充分利用了类似Spark的处理框架的功能，而Hive事务特性则在用户或Hive Metastore启动的Hive任务/查询的下实现。
+根据我们的生产经验，与其他方法相比，将Hudi作为库嵌入到现有的Spark管道中要容易得多，并且操作不会太繁琐。
+Hudi还设计用于与Presto/Spark等非Hive引擎合作，并计划引入除parquet以外的文件格式。
-## Hive Transactions
+## HBase
-[Hive
Transactions/ACID](https://cwiki.apache.org/confluence/display/Hive/Hive+Transactions)
is another similar effort, which tries to implement storage like
-`merge-on-read`, on top of ORC file format. Understandably, this feature is
heavily tied to Hive and other efforts like
[LLAP](https://cwiki.apache.org/confluence/display/Hive/LLAP).
-Hive transactions does not offer the read-optimized storage option or the
incremental pulling, that Hudi does. In terms of implementation choices, Hudi
leverages
-the full power of a processing framework like Spark, while Hive transactions
feature is implemented underneath by Hive tasks/queries kicked off by user or
the Hive metastore.
-Based on our production experience, embedding Hudi as a library into existing
Spark pipelines was much easier and less operationally heavy, compared with the
other approach.
-Hudi is also designed to work with non-hive enginers like Presto/Spark and
will incorporate file formats other than parquet over time.
+尽管[HBase](https://hbase.apache.org)最终是OLTP工作负载的键值存储层，但由于与Hadoop的相似性，用户通常倾向于将HBase与分析相关联。
+鉴于HBase经过严格的写优化，它支持开箱即用的亚秒级更新，Hive-on-HBase允许用户查询该数据。
但是，就分析工作负载的实际性能而言，Parquet/ORC之类的混合列式存储格式可以轻松击败HBase，因为这些工作负载主要是读取繁重的工作。
+Hudi弥补了更快的数据与分析存储格式之间的差距。从运营的角度来看，与管理分析使用的HBase
region服务器集群相比，为用户提供可更快给出数据的库更具可扩展性。
+最终，HBase不像Hudi这样重点支持`提交时间`、`增量拉取`之类的增量处理原语。
-## HBase
+## 流式处理
+
+一个普遍的问题："Hudi与流处理系统有何关系？"，我们将在这里尝试回答。简而言之，Hudi可以与当今的批处理(`写时复制存储`)和流处理(`读时合并存储`)作业集成，以将计算结果存储在Hadoop中。
+对于Spark应用程序，这可以通过将Hudi库与Spark/Spark流式DAG直接集成来实现。在非Spark处理系统(例如Flink、Hive)情况下，可以在相应的系统中进行处理，然后通过Kafka主题/DFS中间文件将其发送到Hudi表中。从概念上讲，数据处理
+管道仅由三个部分组成：`输入`，`处理`，`输出`，用户最终针对输出运行查询以便使用管道的结果。Hudi可以充当将数据存储在DFS上的输入或输出。Hudi在给定流处理管道上的适用性最终归结为你的查询在Presto/SparkSQL/Hive的适用性。
-Even though [HBase](https://hbase.apache.org) is ultimately a key-value store
for OLTP workloads, users often tend to associate HBase with analytics given
the proximity to Hadoop.
-Given HBase is heavily write-optimized, it supports sub-second upserts
out-of-box and Hive-on-HBase lets users query that data. However, in terms of
actual performance for analytical workloads,
-hybrid columnar storage formats like Parquet/ORC handily beat HBase, since
these workloads are predominantly read-heavy. Hudi bridges this gap between
faster data and having
-analytical storage formats. From an operational perspective, arming users with
a library that provides faster data, is more scalable, than managing a big farm
of HBase region servers,
-just for analytics. Finally, HBase does not support incremental processing
primitives like `commit times`, `incremental pull` as first class citizens like
Hudi.
-
-## Stream Processing
-
-A popular question, we get is : "How does Hudi relate to stream processing
systems?", which we will try to answer here. Simply put, Hudi can integrate with
-batch (`copy-on-write storage`) and streaming (`merge-on-read storage`) jobs
of today, to store the computed results in Hadoop. For Spark apps, this can
happen via direct
-integration of Hudi library with Spark/Spark streaming DAGs. In case of
Non-Spark processing systems (eg: Flink, Hive), the processing can be done in
the respective systems
-and later sent into a Hudi table via a Kafka topic/DFS intermediate file. In
more conceptual level, data processing
-pipelines just consist of three components : `source`, `processing`, `sink`,
with users ultimately running queries against the sink to use the results of
the pipeline.
-Hudi can act as either a source or sink, that stores data on DFS.
Applicability of Hudi to a given stream processing pipeline ultimately boils
down to suitability
-of Presto/SparkSQL/Hive for your queries.
-
-More advanced use cases revolve around the concepts of [incremental
processing](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop),
which effectively
-uses Hudi even inside the `processing` engine to speed up typical batch
pipelines. For e.g: Hudi can be used as a state store inside a processing DAG
(similar
-to how
[rocksDB](https://ci.apache.org/projects/flink/flink-docs-release-1.2/ops/state_backends.html#the-rocksdbstatebackend)
is used by Flink). This is an item on the roadmap
-and will eventually happen as a [Beam
Runner](https://issues.apache.org/jira/browse/HUDI-60)
+更高级的用例围绕[增量处理](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop)的概念展开，
+甚至在`处理`引擎内部也使用Hudi来加速典型的批处理管道。例如：Hudi可用作DAG内的状态存储(类似Flink使用的[rocksDB(https://ci.apache.org/projects/flink/flink-docs-release-1.2/ops/state_backends.html#the-rocksdbstatebackend))。
+这是路线图上的一个项目并将最终以[Beam
Runner](https://issues.apache.org/jira/browse/HUDI-60)的形式呈现。
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