HadoopDB：MapReduce与DBMS技术的融合分析

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"HadoopDB是将MapReduce和DBMS技术结合的架构，旨在处理分析型工作负载。这个官方文档详细介绍了HadoopDB的设计理念、架构和优势，适用于大规模数据分析场景。" 在当前的IT环境中，大数据分析已经成为企业的重要需求。随着数据量的急剧增长和对分析能力的需求增加，传统的高端专有机器正在被更经济、低端的 commodity hardware所取代，这些硬件通常以共享无盘的MPP（Massively Parallel Processing）架构布置，甚至在公共或私有云环境中虚拟化运行。在这种背景下，HadoopDB应运而生，它是一种结合了MapReduce和数据库管理系统（DBMS）的技术，旨在高效地处理海量数据的分析任务。 MapReduce是Google提出的一种分布式计算模型，适用于处理大规模数据集。它将复杂计算任务分解为可并行执行的map和reduce操作，极大地提高了数据处理效率。然而，MapReduce在处理交互式查询和复杂分析时效率较低，因为它不擅长处理大量随机读写操作，而这正是DBMS的强项。 HadoopDB的核心思想是将MapReduce的并行计算能力与DBMS的优化查询处理相结合。它通过在Hadoop集群上部署数据库实例，将SQL查询转化为一系列MapReduce任务，从而利用DBMS的优化能力来提升分析性能。这种架构允许HadoopDB在大规模数据集上执行复杂的分析任务，同时保持良好的响应时间和高效率。文档中可能会详细阐述以下几个方面： 1. **系统架构**：HadoopDB的系统架构设计，包括如何将数据库操作与MapReduce任务集成，以及如何在分布式环境中协调和管理这些任务。 2. **查询优化**：HadoopDB如何利用DBMS的查询优化器来改进MapReduce的性能，包括查询计划的生成、数据局部性和并行度控制等。 3. **数据存储**：HadoopDB的数据存储机制，可能涉及到HDFS（Hadoop Distributed File System）的使用，以及如何在Hadoop和DBMS之间高效地移动数据。 4. **性能评估**：文档可能会包含对HadoopDB性能的基准测试和比较，与其他数据分析技术（如纯Hadoop MapReduce或传统DBMS）的性能对比。 5. **应用场景**：介绍HadoopDB适合的应用场景，如商业智能、数据挖掘、实时分析等。 6. **扩展性与可维护性**：讨论HadoopDB的可扩展性和故障恢复机制，以及如何适应不断变化的数据规模和工作负载。 7. **未来发展方向**：可能还会探讨HadoopDB的未来研究和开发方向，如何进一步提升性能、降低延迟和提高用户友好性。通过理解HadoopDB的原理和实践，开发者和数据分析师可以更好地利用分布式计算资源，有效地处理大规模数据分析挑战。这个文档对于想要深入理解大数据处理技术的人来说是一份宝贵的参考资料。

does not have to restart a query if one of the nodes involved in query

processing fails.

Given the proven operational beneﬁts and resource consumption

savings of using cheap, unreliable commodity hardware to build

a shared-nothing cluster of machines, and the trend towards

extremely low-end hardware in data centers [14], the probability

of a node failure occurring during query processing is increasing

rapidly. This problem only gets worse at scale: the larger the

amount of data that needs to be accessed for analytical queries, the

more nodes are required to participate in query processing. This

further increases the probability of at least one node failing during

query execution. Google, for example, reports an average of 1.2

failures per analysis job [8]. If a query must restart each time a

node fails, then long, complex queries are difﬁcult to complete.

Ability to run in a heterogeneous environment. As described

above, there is a strong trend towards increasing the number of

nodes that participate in query execution. It is nearly impossible

to get homogeneous performance across hundreds or thousands of

compute nodes, even if each node runs on identical hardware or on

an identical virtual machine. Part failures that do not cause com-

plete node failure, but result in degraded hardware performance be-

come more common at scale. Individual node disk fragmentation

and software conﬁguration errors can also cause degraded perfor-

mance on some nodes. Concurrent queries (or, in some cases, con-

current processes) further reduce the homogeneity of cluster perfor-

mance. On virtualized machines, concurrent activities performed

by different virtual machines located on the same physical machine

can cause 2-4% variation in performance [5].

If the amount of work needed to execute a query is equally di-

vided among the nodes in a shared-nothing cluster, then there is a

danger that the time to complete the query will be approximately

equal to time for the slowest compute node to complete its assigned

task. A node with degraded performance would thus have a dis-

proportionate effect on total query time. A system designed to run

in a heterogeneous environment must take appropriate measures to

prevent this from occurring.

Flexible query interface. There are a variety of customer-facing

business intelligence tools that work with database software and

aid in the visualization, query generation, result dash-boarding, and

advanced data analysis. These tools are an important part of the

analytical data management picture since business analysts are of-

ten not technically advanced and do not feel comfortable interfac-

ing with the database software directly. Business Intelligence tools

typically connect to databases using ODBC or JDBC, so databases

that want to work with these tools must accept SQL queries through

these interfaces.

Ideally, the data analysis system should also have a robust mech-

anism for allowing the user to write user deﬁned functions (UDFs)

and queries that utilize UDFs should automatically be parallelized

across the processing nodes in the shared-nothing cluster. Thus,

both SQL and non-SQL interface languages are desirable.

4. BACKGROUND AND SHORTFALLS OF

AVAILABLE APPROACHES

In this section, we give an overview of the parallel database and

MapReduce approaches to performing data analysis, and list the

properties described in Section 3 that each approach meets.

4.1 Parallel DBMSs

Parallel database systems stem from research performed in the

late 1980s and most current systems are designed similarly to the

early Gamma [10] and Grace [12] parallel DBMS research projects.

These systems all support standard relational tables and SQL, and

implement many of the performance enhancing techniques devel-

oped by the research community over the past few decades, in-

cluding indexing, compression (and direct operation on compressed

data), materialized views, result caching, and I/O sharing. Most

(or even all) tables are partitioned over multiple nodes in a shared-

nothing cluster; however, the mechanism by which data is parti-

tioned is transparent to the end-user. Parallel databases use an op-

timizer tailored for distributed workloads that turn SQL commands

into a query plan whose execution is divided equally among multi-

ple nodes.

Of the desired properties of large scale data analysis workloads

described in Section 3, parallel databases best meet the “perfor-

mance property” due to the performance push required to compete

on the open market, and the ability to incorporate decades worth

of performance tricks published in the database research commu-

nity. Parallel databases can achieve especially high performance

when administered by a highly skilled DBA who can carefully de-

sign, deploy, tune, and maintain the system, but recent advances

in automating these tasks and bundling the software into appliance

(pre-tuned and pre-conﬁgured) offerings have given many parallel

databases high performance out of the box.

Parallel databases also score well on the ﬂexible query interface

property. Implementation of SQL and ODBC is generally a given,

and many parallel databases allow UDFs (although the ability for

the query planner and optimizer to parallelize UDFs well over a

shared-nothing cluster varies across different implementations).

However, parallel databases generally do not score well on the

fault tolerance and ability to operate in a heterogeneous environ-

ment properties. Although particular details of parallel database

implementations vary, their historical assumptions that failures are

rare events and “large” clusters mean dozens of nodes (instead of

hundreds or thousands) have resulted in engineering decisions that

make it difﬁcult to achieve these properties.

Furthermore, in some cases, there is a clear tradeoff between

fault tolerance and performance, and parallel databases tend to

choose the performance extreme of these tradeoffs. For example,

frequent check-pointing of completed sub-tasks increase the fault

tolerance of long-running read queries, yet this check-pointing

reduces performance. In addition, pipelining intermediate results

between query operators can improve performance, but can result

in a large amount of work being lost upon a failure.

4.2 MapReduce

MapReduce was introduced by Dean et. al. in 2004 [8].

Understanding the complete details of how MapReduce works is

not a necessary prerequisite for understanding this paper. In short,

MapReduce processes data distributed (and replicated) across

many nodes in a shared-nothing cluster via three basic operations.

First, a set of Map tasks are processed in parallel by each node in

the cluster without communicating with other nodes. Next, data is

repartitioned across all nodes of the cluster. Finally, a set of Reduce

tasks are executed in parallel by each node on the partition it

receives. This can be followed by an arbitrary number of additional

Map-repartition-Reduce cycles as necessary. MapReduce does not

create a detailed query execution plan that speciﬁes which nodes

will run which tasks in advance; instead, this is determined at

runtime. This allows MapReduce to adjust to node failures and

slow nodes on the ﬂy by assigning more tasks to faster nodes and

reassigning tasks from failed nodes. MapReduce also checkpoints

the output of each Map task to local disk in order to minimize the

amount of work that has to be redone upon a failure.

Of the desired properties of large scale data analysis workloads,

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