Data Intensive App #3 | Storage and Retrieval

    5. Study  Comments

3.0 Storage and Retrieval

  • 색인의 방식에 따라 다음 두가지로 나뉨

    • log structured storage engine
    • Page-oriented storage engine
    • Other indexing structures

3.1 Data Structures That Power your Database

  • db_set
  • Db_get : full table scan O(n) => 색인의 필요성
  • 색인 (index)

3.1.1 Hash Indexes

  • conventional 한 느낌?

3.1.2 Log Structured Storage Engine : SSTables and LSM-Trees

  • Sorted String Table : merge-sort 느낌, (figure 3-4) , 사전에서 handbag을 찾는다면 h로 먼저가서 찾음, 처음부터 하나씩 볼 필요가 없음
  • Log-Structured Merge Tree

3.1.3 Page oriented storage engine : B-Trees

3.1.4 Comparing B-Trees and LSM-Trees

  • LSM은 쓰기가 빠르고 Btree는 읽기가 빠름

3.1.5 Other Indexing Structures

  • storing values within the index

  • multi-column indices

  • Full-text search and fuzzy indices

  • keeping everything in memory

3.2 Transaction Processing or Analytics

3.3 Column-Oriented Storage

  • Base

3.4 Summary

  • In this chapter we tried to get to the bottom of how database handle storage and retrieval.
  • we saw that storage engine fall into twh broad categories:
    • OLTP (optimized for transaction processing)
    • OLAP (optimized for analytics)