Database Storage

Data Layouts, Indexes, and More

Outline

  • DB 101
  • DB Classifications
    • In-Memory vs On-Disk
    • OLTP vs OLAP
  • Data Storage
    • SSDs vs HDDs
  • Data Layouts
  • Indexes and Compression
    • General Theory
    • Hash Tables
    • B-Tree Family
    • LSM Trees

Databases 101

Data Management

  • Safety
  • Auditablity
  • Access control
  • Knowledge sharing

Types of Queries

  • Point Queries
SELECT *
FROM tbl
WHERE x = 42;
SELECT *
FROM tbl
WHERE x > 42 
  AND x < 99;
SELECT
  x, y, z,
  COUNT(*)
FROM tbl
GROUP BY x, y, z
  • Range Queries
    • >, <, >=, <=
  • Aggregations
    • GROUP BY x, y, z
    • MIN, MAX, AVG, COUNT, SUM, AVG

Read/Write Usage

  • Volume
  • Queries per Second (QPS)

Write Strategies

  • Mutable (In-Place Updates)
  • Immutable (Append-Only / Log)

Database Classifications

In-Memory vs On-Disk

  • Bullet One
  • Bullet Two
  • Bullet Three

In-Memory vs On-Disk

  • Bullet One
  • Bullet Two
  • Bullet Three

OLTP

  • Transactions
    • Point and Range Queries
    • In-place Updates
    • High Read Rate
      • > 10k TPS
    • Medium Storage Needs
      • 10s of TBs

See more at https://dbdb.io/browse?tag=oltp

OLTP

See more at https://dbdb.io/browse?tag=oltp

  • Transactions
    • Point and Range Queries
    • In-place Updates
    • High Read Rate
      • > 10k TPS
    • Medium Storage Needs
      • 10s of TBs

OLAP

See more at https://dbdb.io/browse?tag=olap

  • Aggregations (full table scans)
  • Append-only storage
  • Write-Optimized, Low Read Rate
    • 100s Read QPS
  • Large Storage Needs
    • 100s of PBs

OLAP

Redshift
BigQuery

BigQuery

Redshift

See more at https://dbdb.io/browse?tag=olap

Hive

Snowflake

ClickHouse

DuckDB

Impala

Pinot

  • Aggregations (full table scans)
  • Append-only storage
  • Write-Optimized, Low Read Rate
    • 100s Read QPS
  • Large Storage Needs
    • 100s of PBs

https://cs.brown.edu/~ugur/fits_all.pdf

dbdb.io

Database Architecture

DBMS Architecture

  • Transport Subsystem
  • Query Processor
  • Execution Engine
  • Storage Engine

Image from Database Internals (1 - Introduction and Overview/DBMS Architecture)

Transport

  • Transport Subsystem
    • Handling client requests
    • Communication with nodes across cluster

Image from Database Internals (1 - Introduction and Overview/DBMS Architecture)

Query Processing

  • Query Processor
    • Parses, interprets, validates, and optimizes queries

Image from Database Internals (1 - Introduction and Overview/DBMS Architecture)

Execution

  • Execution Engine
    • Runs execution plan
    • Collects the results of the execution of local and remote operations

Image from Database Internals (1 - Introduction and Overview/DBMS Architecture)

Storage

  • Storage Engine
    • dasfajsdnlasd

Image from Database Internals (1 - Introduction and Overview/DBMS Architecture)

Data Storage

Storage Hierarchy

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

CPU Registers

CPU Cache

DRAM

SSD

HDD

Network Storage

Storage Hierarchy

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

CPU Registers

CPU Cache

DRAM

SSD

HDD

Network Storage

Disk

Memory

CPU

{

{

{

Storage Hierarchy

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

CPU Registers

CPU Cache

DRAM

SSD

HDD

Network Storage

Faster

Smaller

More Expensive

Slower

Larger

Cheaper

Interlude: SSDs vs HDDs

  • Hard Disk Drive
    • Slow Random Access (Spinning Disk w/ Mechanical Arm)
    • Longevity (Magnetic Storage)

Interlude: SSDs vs HDDs

  • Solid State Drive
    • No Moving Parts
      • 100x faster random access
      • Parallel Accesses
    • Cons
      • Limited Lifetime (Write Endurance + Power Loss)
      • Expensive

SSD Organization Schematics

Image from Database Internals (2 - B-Tree Basics/Disk-Based Structures/Solid State Drives)

SSD Organization Schematics

Image from Database Internals (2 - B-Tree Basics/Disk-Based Structures/Solid State Drives)

Page: 2 - 16 Kb

Block: >= 1 MB

  • 64 - 512 Pages

Planes:

Die:

 

Memory Cell: 1 bit

Strings: 32 or 64 cells

Arrays: Multiple strings

Page: Multiple arrays

SSDs: Flash Translation Layer

  • Bullet One
  • Bullet Two
  • Bullet Three

Interlude: SSDs vs HDDs

Attribute Solid-State Drive Hard Disk Drive
Price per capacity 30 cents / GB 3 cents / GB
Start-up time Almost Instantaneous Several Seconds
Sequential-access performance 200 - 3500 MB/sec 200 - 480 MB/sec
Random-access performance < 0.1 ms 3 - 12 ms
Parallel Access Yes No

https://en.wikipedia.org/wiki/Solid-state_drive

Storage Hierarchy

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

CPU Registers

CPU Cache

DRAM

SSD

HDD

Network Storage

Faster

Smaller

More Expensive

Slower

Larger

Cheaper

Storage Hierarchy

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

CPU Registers

CPU Cache

DRAM

SSD

HDD

Network Storage

Faster

Smaller

More Expensive

Slower

Larger

Cheaper

Volatile

Random Access

Byte-Addressable

Non-Volatile

Sequential Access

Block-Addressable

Storage Hierarchy

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

CPU Registers

CPU Cache

DRAM

SSD

HDD

Network Storage

Volatile

Random Access

Byte-Addressable

Non-Volatile

Sequential Access

Block-Addressable

Tiered Storage

  • Data Lake with SSDs + HDDs
    • SSDs for new, active data
    • HDDs for older, archival data
  • Ex. ClickHouse Cloud
    • Offers read-through + write-through NVMe SSD caches on top of S3 network storage
  • In-Memory Buffer Pools
    • Every on-disk store will have this

Disaggregated Storage

  • Bullet One
  • Bullet Two
  • Bullet Three

Access Times

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

"Latency Numbers Every Programmer Should Know"

1 ns L1 Cache Ref
4 ns L2 Cache Ref
100 ns DRAM
16,000 ns SSD
2,000,000 ns HDD
~50,000,000 ns Network Storage
1,000,000,000 ns Tape Archives

Access Times x 1 Million

Slides copied from Database Storage Part 1 - @CMUDatabaseGroup

"Latency Numbers Every Programmer Should Know"

1 ns L1 Cache Ref 1 sec
4 ns L2 Cache Ref 4 sec
100 ns DRAM 100 sec
16,000 ns SSD 4.4 hours
2,000,000 ns HDD 3.3 weeks
~50,000,000 ns Network Storage 1.5 years
1,000,000,000 ns Tape Archives 31.7 years

"Latency Numbers Every Programmer Should Know"

Jeff Dean's Latency Numbers via github:colin-scott/interactive_latencies

https://blog.bytebytego.com/p/ep22-latency-numbers-you-should-know

1 sec

10 sec

100 sec

 3 hours

1 day

1.5 weeks

16.5 weeks

3 years

3 decades

3 centuries

x1 Million

Storage Engine

Buffer (Pool) Manager

  • Bullet One
  • Bullet Two
  • Bullet Three

On-Disk Storage

  • Bullet One
  • Bullet Two
  • Bullet Three

By Data Structure

Data Layout

Row vs Columnar

  • Bullet One
  • Bullet Two
  • Bullet Three

Slotted Pages

  • Bullet One
  • Bullet Two
  • Bullet Three

Log-Structured Storage

  • Bullet One
  • Bullet Two
  • Bullet Three

Columnar File Formats

  • Bullet One
  • Bullet Two
  • Bullet Three

Scaling Databases

Scaling Up (Single Server)

Database Example Peak QPS
(w/o experts)		 Storage Capacity
Cache (KV Store) Valkey / Redis 150k QPS ~13 GB
(AWS ElasticCache)
OLTP Postgres 50k TPS 64 TiB
(Aurora on RDS)
OLAP ClickHouse on EBS 5k QPS 64 TiB
(Max EBS Volume)
ClickHouse on S3

Unscientific and Totally Made-Up Capacity

Scaling Out

  • Read Replicas
    • QPS++
  • Partitioning
    • Storage++
    • ClickHouse can handle 100s of PBs
      • Requires object storage (S3) instead of block storage

Compression & Indexes

An Intuition

Compression

  • Bullet One
  • Bullet Two
  • Bullet Three

Indexes

  • Auxillary Data Structure
    • Support queries on a (usually) separate data structure
  • Index Flavors
    • Ordering
    • Grouping

Compressed Index

  • Queryable in compressed form
  • Ordering helps both compression and indexing
    • By surfacing the latent structure of our data

See more from Ben Langmead JHU Burrows-Wheeler Indexing Course

 

Compact Data Structures 🤝 Databases

Index Types

  • Adaptive Radix Tree (ART)
  • AVL-Tree
  • B-Epsilon Tree
  • BitMap
  • Block Range Index (BRIN)
  • B+Tree
  • Bw-Tree
  • Hash Table
  • Hierarchical Navigable Small World (HNSW)
  • Inverted Index (Full Text)
  • K-D Tree
  • Log-Structured Merge Tree
  • MassTree
  • Patricia/Radix Trie
  • Red-Black Tree
  • R-Tree
  • Skip List
  • T-Tree

Extended Indexes

  • Partial Indexes
  • Functional Indexes
  • Skip Indexes
    • Fence Pointers
    • Min / Max
    • Block Range Indexes
    • Bloom Filters

Index Lookups

  • Full Table Scan
    • (Parallel) Sequential Scan
  • Index Scan
  • Index-Only Scan
  • Bitmap Index Scan

Sequential Scan Optimizations

  • Prefetching
  • Buffer Pool Bypass
  • Parallelization
  • Heap Clustering
  • Late Materialization
  • Data Skipping
    • Approximation
    • BRIN / Zone Maps

Copied from CMU DB Course - Query Execution Part 1

Compression

  • Bullet One
  • Bullet Two
  • Bullet Three

Hash Indexes

Limitations of Hash Indexes

  • No Range Scans
    • Only point queries
  • No Prefix Lookups
    • Need entire key

B-Tree Indexes

Block Range Indexes

BRIN / Zone Maps

  • Pre-Compute
  • Bullet Two
  • Bullet Three

LSM Indexes

Log-Structured Merge Tree

  • Always IOT / clustered index????
    • Data stored in sorted key order on disk
  • Bullet Two
  • Bullet Three

SpeedDB/RocksDB: Rise of the LSM Tree