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1 PART 1_ BigTable_HBase Bloom Filter _10+10+10 points_ 2 PART ..

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BigTable non-relational database, is a sparse, distributed, persistent storage of the multi-dimensional sorted Map. Bigtable is designed to reliably handle PB-level data, and can be deployed to thousands of machines. Bigtable has achieved several of the following goals: wide applicability, scalability, high performance and high availability. Bigtable has more than 60 Google products and projects has been applied, including Google Analytics, GoogleFinance, Orkut, Personalized Search, Writely and GoogleEarth. These products are made ??of Bigtable different needs, some need high throughput batch processing, while others require a timely response and rapid return data to the end user. They use the Bigtable cluster configuration is also very different, and some clusters only a few servers, while others require thousands of servers, storage, hundreds of TB of data.

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Public Domain

NOSQL – Advanced Lecture, WS 10/11

Information Systems Group Prof. Dr. Jens Dittrich

Saarland University TAs: Alekh Jindal and Jorge Quiané

Exercise 6: Bloom Filters, BerkeleyDB, HiveQL

1 PART 1: BigTable/HBase Bloom Filter (10+10+10 points)

As explained in the lecture in BigTable/HBase bloom ﬁlter creation may be piggy-backed on the merge

of the indexes residing on disk.

1. Describe how this process may be exploited to deﬁne a bloom ﬁlter with the optimal parameters of

n, m, and k.

2. Derive a formula (show the derivation steps) to ﬁnd the optimal value of m and k guaranteeing a

"false positive" probability below 0.1%.

3. What are the trade-oﬀs of the k and m parameters in a working system?

2 PART 2: BerkeleyDB Concepts (10+10+10 points)

Answer the following questions:

1. Brieﬂy describe the Btree, Hash, Queue, and Recno access methods in BerkeleyDB.

2. Which data management services are provided by BerkeleyDB?

3. What are the major diﬀerences between BerkeleyDB and Relational Databases?

3 PART 3: HiveQL Hands-on (40 points)

1. Write down the equivalent HiveQL queries for the slightly modiﬁed TPC-H Query 1 and TPC-H

Query 3 from Exercise 1.

2. Install Hive 0.6.0 from http://hive.apache.org/. Load TPC-H customers, orders, and lineitem table

into HDFS. Run your HiveQL queries from above, report and compare runtimes with those in

Exercise 1. Also, hand in the EXPLAIN EXTENDED output for your queries.

1