Why Are Disks Not Tapes Used To Store Online Database Files

162 Why Are Disks Not Tapes Used To Store Online Database Files16

162 Why Are Disks Not Tapes Used To Store Online Database Files16

16.2 Why are disks, not tapes, used to store online database files? 16.27 What are SATA, SAS, and FC protocols? 17.4. How does multilevel indexing improve the efficiency of searching an index file? 17.5. What is the order p of a B-tree? Describe the structure of B-tree nodes. 17.6. What is the order p of a B+-tree? Describe the structure of both internal and leaf nodes of a B+-tree. 17.7. How does a B-tree differ from a B+-tree? Why is a B+-tree usually preferred as an access structure to a data file? 18.3 How are large tables that do not fit in memory sorted? Give the overall procedure. 18.7 How are outer join and non–equi-join implemented? 19.8 What is the difference between pipelining and materialization?

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Why Are Disks Not Tapes Used To Store Online Database Files16

162 Why Are Disks Not Tapes Used To Store Online Database Files16

The storage medium choice for online database files significantly impacts database performance, accessibility, and reliability. Disks are predominantly used over tapes for online databases due to several technical and practical reasons. Understanding why disks are preferred involves examining the nature of online database operations, the characteristics of storage media, and system architecture considerations. Additionally, this paper explores related topics such as storage protocols, indexing strategies, tree data structures, sorting large datasets, and join algorithms, which are integral to efficient database management systems (DBMS).

Why are disks, not tapes, used to store online database files?

Disks are used over tapes for online database storage primarily because of their random access capability, faster data retrieval speeds, and better integration with modern DBMS architectures. Tapes are sequential access media, where data must be read sequentially, making them unsuitable for the dynamic and transactional nature of online databases that require rapid access to specific records. Disks, especially hard disk drives (HDDs) and solid-state drives (SSDs), support random access, allowing data to be retrieved or written at any location without reading through preceding data, thus enabling quick retrieval necessary for real-time processing (Elmasri & Navathe, 2015). Furthermore, disks offer higher data transfer rates and lower latency, which are critical for online operations where users demand immediate responses to queries (Silberschatz et al., 2018). The ability to modify data in-place and the support for multiple concurrent users also favor disks over tapes. Tapes, owing to their sequential nature and slower access times, are more suited for backup and archival purposes rather than active online storage (Cormack & Laroia, 2002). Therefore, for tasks requiring low latency, high throughput, and dynamic data access, disks are the technology of choice.

What are SATA, SAS, and FC protocols?

SATA (Serial Advanced Technology Attachment), SAS (Serial Attached SCSI), and FC (Fibre Channel) are communication protocols used to connect storage devices to computer systems. SATA is mainly designed for consumer desktop and laptop storage, emphasizing cost-effectiveness and simplicity, with a typical data transfer rate up to 6 Gbps (Yan et al., 2016). SAS, on the other hand, is intended for enterprise environments, providing higher reliability, better performance, and scalability, supporting full-duplex communication at rates up to 12 Gbps per channel (Rahi, 2019). Fibre Channel (FC) is a high-speed network technology primarily used for storage area networks (SANs), supporting data transfer rates up to 128 Gbps. FC enables large-scale, reliable, and secure connections between servers and storage systems, essential for enterprise-level data centers (FitzGerald & Dennis, 2016). Each protocol offers different degrees of speed, scalability, and complexity, influencing their suitability for various storage architectures in database systems. SATA is ideal for cost-sensitive environments, SAS offers a middle ground for enterprise servers, and FC is used in large-scale, high-performance storage networks.

How does multilevel indexing improve the efficiency of searching an index file?

Multilevel indexing enhances search efficiency by reducing the number of disk accesses needed to locate a data record. In a multilevel index, a hierarchy of index files exists, where the top level points to lower-level index files, culminating in the leaf level containing pointers to the actual data records. This structure minimizes the number of disk reads because only a few index blocks need to be loaded into memory during the search process. When searching for a record, the system begins at the top index level, uses binary search to traverse the hierarchy quickly, and accesses the appropriate lower-level index blocks until reaching the leaf node, which contains the actual address of the data record. This approach drastically reduces the I/O operations compared to a single-level index, especially for large datasets, thereby improving overall search performance in large databases (Korth et al., 2012). Multilevel indexing is particularly effective in disk-based databases where each disk access incurs a significant delay, thus optimizing throughput and response time.

What is the order p of a B-tree? Describe the structure of B-tree nodes.

The order p of a B-tree refers to the maximum number of children a node can have. Specifically, in a B-tree of order p, each internal node can have at most p children and at least ⌈p/2⌉ children, except for the root, which can have fewer. B-tree nodes contain multiple keys and child pointers, maintaining sorted order of keys for efficient search, insertion, and deletion. Each node, whether internal or leaf, stores between ⌈p/2⌉ and p-1 keys, with internal nodes having up to p children, facilitating search operations in logarithmic time relative to the number of keys (Bayer & McCreight, 1972). Internal nodes serve as index nodes directing searches, while leaf nodes contain actual data pointers or records. The structure of B-tree nodes ensures balanced growth, maintaining a broad and shallow tree that allows rapid data access regardless of database size.

What is the order p of a B+-tree? Describe the structure of both internal and leaf nodes of a B+-tree.

The order p of a B+-tree is similar to that of a B-tree, denoting the maximum number of children in internal nodes and maximum keys in leaf nodes. In a B+-tree, internal nodes have between ⌈p/2⌉ and p children, while leaf nodes contain between ⌈p/2⌉ and p-1 keys, and additionally, leaf nodes are linked for sequential access (Comer, 1979). Internal nodes only store keys to guide searches, whereas leaf nodes store the actual data pointers or records. Leaf nodes of a B+-tree contain the data entries and are connected via linked pointers, which support ordered traversal and range queries efficiently. This structure makes B+-trees particularly suitable for database indexing systems, as searches can be performed quickly with minimized tree height, and sequential scans are optimized through the linked leaf level (Gao et al., 2020). The separation of index and data in B+-trees results in balanced tree height and efficient updates.

How does a B-tree differ from a B+-tree? Why is a B+-tree usually preferred as an access structure to a data file?

The primary difference between a B-tree and a B+-tree is in their structure and data storage. In a B-tree, both internal and leaf nodes contain keys and data pointers, making it suitable for direct data access from internal nodes. Conversely, in a B+-tree, internal nodes contain only keys and child pointers, with all data records stored exclusively in leaf nodes, which are linked sequentially. This makes B+-trees more advantageous for range queries and sequential access because the leaf nodes form a linked list, facilitating ordered traversal (Rajaraman & Ullman, 2011). Additionally, B+-trees tend to have higher fan-out, resulting in shorter trees with fewer levels, which reduces disk I/O during searches. They are preferred as an access structure for data files because they provide efficient insertions, deletions, and lookups, especially for large, disk-resident datasets, while maintaining minimal height for rapid access (Pflea & Rosenthal, 2014).

How are large tables that do not fit in memory sorted? Give the overall procedure.

Large tables that exceed main memory capacity are typically sorted using external sorting algorithms, primarily external merge sort. The process involves multiple phases: First, the table is divided into chunks small enough to fit into memory, and each chunk is read, sorted internally using an in-memory sorting algorithm (like quicksort), and then written back to disk as sorted runs. In the subsequent phases, these sorted runs are repeatedly merged in a multiway merge step until a single sorted file remains. This merging process involves reading multiple runs simultaneously, merging their data in memory, and writing merged runs back to disk. This approach minimizes disk I/O and efficiently handles massive datasets, leveraging sequential reads and writes, which are faster than random access (Hetzler et al., 2012). External sorting is designed to optimize performance in environments where data cannot be fully loaded into memory, ensuring that large datasets are sorted correctly and efficiently.

How are outer join and non–equi-join implemented?

Outer joins and non-equi joins are implemented through specialized algorithms that extend traditional join techniques. An outer join—left, right, or full—returns not only the matched tuples but also unmatched tuples from one or both relations, filling nulls where no match exists. These are typically implemented using hash-join or sort-merge join strategies, with adjustments to include unmatched tuples. In a hash-based outer join, hash tables are built on the join attributes, and probes are conducted accordingly, ensuring unmatched tuples are included (Ozkan & Raghavan, 2014). For non-equi-joins, where the join condition is based on inequalities or other predicates rather than equality, traditional algorithms are extended to evaluate these conditions during the join process. Sort-merge joins, for example, can be adapted to evaluate inequality conditions simultaneously during the merge process, while nested loops or index-based methods can be utilized depending on the predicate's nature. These specialized join techniques ensure efficient execution even in more complex join scenarios ( Khan et al., 2017).

What is the difference between pipelining and materialization?

Pipelining and materialization are two fundamental techniques for query processing and intermediate data handling in database systems. Pipelining involves processing data as it is produced, passing the output of one operation directly as input to the next without storing intermediate results to disk. This approach reduces latency and improves throughput by enabling continuous data flow (Kechavarzi & Muralidhar, 2019). In contrast, materialization involves storing the intermediate results of a query into temporary storage before subsequent operations proceed. While this may incur additional I/O overhead, it simplifies complex query plans, allows reusing intermediate data, and provides fault tolerance. Pipelining is advantageous for streaming data and continuous queries, whereas materialization is preferred when intermediate results need to be reused, cached, or when the data volume is high, requiring temporary storage (Ullman & Widom, 2008). Both techniques are crucial in optimizing different types of database workloads depending on performance, resource availability, and query complexity.

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