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Database Storage Engine Internals

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AdewumiSunkanmi

The document provides a comprehensive overview of data structures and algorithms used in database storage engines, focusing on the log-structured merge (LSM) tree. It discusses various database selection criteria, including data structure types, scalability, and developer familiarity, along with the components and functioning of LSM trees. Additionally, it explains how data operations such as write, update, and delete are handled within the LSM tree architecture.

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Demystifying data structures and algorithms adopted By database storage engine Adewumi Sunkanmi D.
Demystifying data structures and algorithms used by database storage engine
Adewumi Sunkanmi D. Senior Software Engineer at Acronis working on Advanced Automation, one of the cloud services offered by Acronis Cyber Cloud.
Outline 1. Overview of a three-tier application 2. Criteria for selecting the best database for an application 3. Overview of database architecture 4. Types for database storage engines and their tradeoffs 5. Q/A
client
POST GET client server
POST GET WRITE READ server client Database
POST GET WRITE READ server client Database Which database should we use🤔?
POST GET WRITE READ server client Database Which database should we use🤔?
POST GET WRITE READ server client Database Which database should we use🤔?
POST GET WRITE READ server client Database Which database should we use🤔?
POST GET WRITE READ server client Database Which database should we use🤔?
POST GET WRITE READ server client Database Which database should we use🤔? BigTable
POST GET WRITE READ server client Database Which database should we use🤔? BigTable Neo4J
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph?
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes)
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes) - Replication(Copies of data on multiple nodes)
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes) - Replication(Copies of data on multiple nodes) 3. Support and familiarity of developers with database
How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes) - Replication(Copies of data on multiple nodes) 3. Support and familiarity of developers with database 4. Rate of write and read and how EXACTLY are these operations handled at the hardware level?
https://www.oreilly.com/library/view/high-performance-mysql/9781449332471/ch01.html
SELECT COLS FROM WHERE COL_ID students > score 70 firstname lastname “SELECT firstname, lastname FROM students WHERE score > 70;”
https://www.oreilly.com/library/view/high-performance-mysql/9781449332471/ch01.html
https://www.oreilly.com/library/view/high-performance-mysql/9781449332471/ch01.html Disk
Types of storage engines - Log Structured Merge (LSM) Tree - Page Oriented (B-Tree)
https://www.cs.umb.edu/~poneil/lsmtree.pdf
Log Structured Merge Tree Storage Engine The LMS tree is an immutable disk resident data structure and it is optimized for sequential writes while maintaining the acceptable read performance.
Log Structured Merge Tree Storage Engine Three components 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write ben: 300 Memtable e.g Red black tree in RAM
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write ben: 300 Memtable e.g Red black tree in RAM josh: 500
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red black tree in RAM ben: 300 josh: 500 Threshold reached!
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 SSD/HDD file (SSTable file) T1 ben: 300 bin: 220 josh: 500
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB 10MB 10MB 10MB 10MB alexandar : 10 andreas : 50 ……. erik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ………
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (segment file) 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB Find(apa)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 300 mia: 220 write
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 How do we handle update? Since we return from the most recent memtable or segment file, we just insert the key with the new value, Ben will be returned from T2 not T1 SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 How do we handle delete? Insert the key with a delete marker called tombstone, since this will be the most recent, we can tell it has been deleted, e.g ben->null SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 But now we have duplicates, space wastage :( SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 But now we have duplicates, space wastage :( Yes, but compaction will help SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 But now we have duplicates, space wastage :( Yes, but compaction will help Compaction SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 What if we don’t find the key, we search all the SSTable files? Compaction SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine Key present?: Strict NO if not 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 What if we don’t find the key, we search all the SSTable files? Compaction Optimtimize reads with Bloom Filters Maybe or Maybe not(99% accurate) https://brilliant.org/wiki/bloom-filter/ SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 What if power failure happens before data is flushed to disk? Compaction 1. Persist write in an append only log file before writing to in-memory table. WAL 2. Recreate memtable from last Log Sequence Number. SSD/HDD file (SSTable file)
Log Structured Merge Tree Storage Engine Where is LSM tree Storage engine used? 1. Apache Cassandra 2. WiredTiger 3. InfluxDB 4. Yugabyte DB 5. ScyllaDB 6. CockroachDB 7. Google’s BigTable 8. RocksDB
Types of storage engines - Log Structured Merge (LSM) Tree - Page Oriented (B-Tree)
https://carlosproal.com/ir/papers/p121-comer.pdf
https://carlosproal.com/ir/papers/p121-comer.pdf B-Trees B trees are page-oriented indexing structures
https://carlosproal.com/ir/papers/p121-comer.pdf B-Trees Important notes on B-tree 1. Store key value pairs (sorted by key) 2. Self balancing 3. Often used for indexing 4. Mutable data structure(in place update) 5. Each node is a fixed size block/page 4KB 6. Can only read or write one page at a time
https://carlosproal.com/ir/papers/p121-comer.pdf Anatomy of B-Tree 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 69 70 78 85 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) key [69, 90) val val val
B-Trees https://sqlbak.com/academy/database-page A database page
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 69 70 78 85 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) key [69, 90) val val val NOTE: Leaf Page contains both the key and value Anatomy of B-Trees
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val Branching factor = 5 Depth= 3 Anatomy of B-Tree
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val READ(78) Anatomy of B-Tree
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val READ(78) Anatomy of B-Tree
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val READ(78) Anatomy of B-Tree
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val found! READ(78) Anatomy of B-Tree
https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val found! READ(78) Anatomy of B-Tree Searching for a key is faster because we are not scaning all keys but only keys within range, takes O(log n) Where n is the total number of keys
https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 90 val val val val 70 78 85 INSERT(87) 87 69 val val val val 70 78 85 86 val Branching factor - 5
https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 90 Branching factor exceeded! > 5 Create new page val val val val 70 78 85 87 69 val val val val 70 78 85 86 val 87 val Branching factor - 5 INSERT(87)
https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 90 69 70 78 val val val 85 86 87 val val val INSERT(87) Branching factor exceeded! > 5 Create new page
https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 85 69 70 78 val val val 85 86 87 val val val 90 Add 85 to parent page INSERT(87)
https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 85 69 70 78 val val val 85 86 87 val val val 90 Add 85 to parent page What if the parent page is full? Split it INSERT(87)
https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 85 69 70 78 val val val 85 86 87 val val val 90 Add 85 to parent page How does update work? 1. Find the leaf page with key 2. Edit the row 3. Overwrite the page INSERT(87)
LSM trees Vs B-Trees storage engine LSM Tree B-Tree Optimized for write Optimized for read Compressed better(No Fragmentation) Fragmentation wastes space There can be duplicates before compaction Each key exist exactly in one place Strong transaction support Spikes in write can cause slow compaction due to many SSTable files. Can cause Out of Memory Error(OOM)
Space optimization in B-tree Primary index(primary key index) Secondary index
Space optimization in B-tree Secondary index Primary index(primary key index) Leaf page contains both key and value Leaf page contains both key and value DUPLICATE !
Space optimization in B-tree Secondary index Primary index(primary key index) Store value offset(smaller in size) Store value offset (smaller in size) val1 val2 val3 val4 val5 … Heap File
Space optimization in B-tree Secondary index Primary index(primary key index) Store value offset(smaller in size) Store value offset (smaller in size) val1 val2 val3 val4 val5 … Heap File Store value offset(smaller in size) Extra Disk I/O So you can store important columns in leaf page and less important columns in heap file
@gifted_dl @gifted_dl Adewumi Sunkanmi D.

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Database Storage Engine Internals

  • 1. Demystifying data structures and algorithms adopted By database storage engine Adewumi Sunkanmi D.
  • 2. Demystifying data structures and algorithms used by database storage engine
  • 3. Adewumi Sunkanmi D. Senior Software Engineer at Acronis working on Advanced Automation, one of the cloud services offered by Acronis Cyber Cloud.
  • 4. Outline 1. Overview of a three-tier application 2. Criteria for selecting the best database for an application 3. Overview of database architecture 4. Types for database storage engines and their tradeoffs 5. Q/A
  • 15. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph?
  • 16. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database
  • 17. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling
  • 18. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes)
  • 19. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes) - Replication(Copies of data on multiple nodes)
  • 20. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes) - Replication(Copies of data on multiple nodes) 3. Support and familiarity of developers with database
  • 21. How do we select the best database for an application? 1. Structure of the data we want to store; structured, unstructured, graph? 1. Scalability of the database - Horizontal or Vertical scaling - Sharding(Partition data across nodes) - Replication(Copies of data on multiple nodes) 3. Support and familiarity of developers with database 4. Rate of write and read and how EXACTLY are these operations handled at the hardware level?
  • 23. SELECT COLS FROM WHERE COL_ID students > score 70 firstname lastname “SELECT firstname, lastname FROM students WHERE score > 70;”
  • 26. Types of storage engines - Log Structured Merge (LSM) Tree - Page Oriented (B-Tree)
  • 28. Log Structured Merge Tree Storage Engine The LMS tree is an immutable disk resident data structure and it is optimized for sequential writes while maintaining the acceptable read performance.
  • 29. Log Structured Merge Tree Storage Engine Three components 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table)
  • 30. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177
  • 31. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write ben: 300 Memtable e.g Red black tree in RAM
  • 32. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write ben: 300 Memtable e.g Red black tree in RAM josh: 500
  • 33. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red black tree in RAM ben: 300 josh: 500 Threshold reached!
  • 34. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 SSD/HDD file (SSTable file) T1 ben: 300 bin: 220 josh: 500
  • 35. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB
  • 36. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB 10MB 10MB 10MB 10MB alexandar : 10 andreas : 50 ……. erik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ………
  • 37. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB
  • 38. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 177 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (segment file) 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB Find(apa)
  • 39. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 SSD/HDD file (SSTable file) 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 300 mia: 220 write
  • 40. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 SSD/HDD file (SSTable file)
  • 41. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) SSD/HDD file (SSTable file)
  • 42. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 SSD/HDD file (SSTable file)
  • 43. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 SSD/HDD file (SSTable file)
  • 44. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 SSD/HDD file (SSTable file)
  • 45. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 SSD/HDD file (SSTable file)
  • 46. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 How do we handle update? Since we return from the most recent memtable or segment file, we just insert the key with the new value, Ben will be returned from T2 not T1 SSD/HDD file (SSTable file)
  • 47. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 How do we handle delete? Insert the key with a delete marker called tombstone, since this will be the most recent, we can tell it has been deleted, e.g ben->null SSD/HDD file (SSTable file)
  • 48. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 But now we have duplicates, space wastage :( SSD/HDD file (SSTable file)
  • 49. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 But now we have duplicates, space wastage :( Yes, but compaction will help SSD/HDD file (SSTable file)
  • 50. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 But now we have duplicates, space wastage :( Yes, but compaction will help Compaction SSD/HDD file (SSTable file)
  • 51. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 What if we don’t find the key, we search all the SSTable files? Compaction SSD/HDD file (SSTable file)
  • 52. Log Structured Merge Tree Storage Engine Key present?: Strict NO if not 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 What if we don’t find the key, we search all the SSTable files? Compaction Optimtimize reads with Bloom Filters Maybe or Maybe not(99% accurate) https://brilliant.org/wiki/bloom-filter/ SSD/HDD file (SSTable file)
  • 53. Log Structured Merge Tree Storage Engine 1. Memtable 2. In-memory index 3. SSTable (Sorted String Table) ben 300 josh 500 bin 220 ben 177 mia 220 eve 173 write bin: 220 Memtable e.g Red Black tree in RAM ben: 300 josh: 500 flush ben: 300 bin: 220 josh: 500 T1 40MB 10MB 10MB 10MB 400 alexandar : 10 andreas : 50 ……. arik : 500 erling : 200 …….. jan : 11 johan : 300 …….. robert: 499 roy: 200 ……… In-memory index Key Byte offset alexandar 0 arik 303 jan robert 500 10MB eve: 173 ben: 177 mia: 220 write ben: 177 eve: 173 mia: 200 flush T2 Read(ben) Step 1 Step 2 Step 3 What if power failure happens before data is flushed to disk? Compaction 1. Persist write in an append only log file before writing to in-memory table. WAL 2. Recreate memtable from last Log Sequence Number. SSD/HDD file (SSTable file)
  • 54. Log Structured Merge Tree Storage Engine Where is LSM tree Storage engine used? 1. Apache Cassandra 2. WiredTiger 3. InfluxDB 4. Yugabyte DB 5. ScyllaDB 6. CockroachDB 7. Google’s BigTable 8. RocksDB
  • 55. Types of storage engines - Log Structured Merge (LSM) Tree - Page Oriented (B-Tree)
  • 58. https://carlosproal.com/ir/papers/p121-comer.pdf B-Trees Important notes on B-tree 1. Store key value pairs (sorted by key) 2. Self balancing 3. Often used for indexing 4. Mutable data structure(in place update) 5. Each node is a fixed size block/page 4KB 6. Can only read or write one page at a time
  • 59. https://carlosproal.com/ir/papers/p121-comer.pdf Anatomy of B-Tree 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 69 70 78 85 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) key [69, 90) val val val
  • 61. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 69 70 78 85 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) key [69, 90) val val val NOTE: Leaf Page contains both the key and value Anatomy of B-Trees
  • 62. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val Branching factor = 5 Depth= 3 Anatomy of B-Tree
  • 63. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val READ(78) Anatomy of B-Tree
  • 64. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val READ(78) Anatomy of B-Tree
  • 65. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val READ(78) Anatomy of B-Tree
  • 66. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val found! READ(78) Anatomy of B-Tree
  • 67. https://carlosproal.com/ir/papers/p121-comer.pdf 12 30 13 23 25 27 50 120 31 37 42 49 52 60 69 90 Key < 10 Key [120, inf) key [12, 30) key [30, 50) key [50, 120) 69 70 78 85 key [69, 90) val val val found! READ(78) Anatomy of B-Tree Searching for a key is faster because we are not scaning all keys but only keys within range, takes O(log n) Where n is the total number of keys
  • 68. https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 90 val val val val 70 78 85 INSERT(87) 87 69 val val val val 70 78 85 86 val Branching factor - 5
  • 69. https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 90 Branching factor exceeded! > 5 Create new page val val val val 70 78 85 87 69 val val val val 70 78 85 86 val 87 val Branching factor - 5 INSERT(87)
  • 70. https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 90 69 70 78 val val val 85 86 87 val val val INSERT(87) Branching factor exceeded! > 5 Create new page
  • 71. https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 85 69 70 78 val val val 85 86 87 val val val 90 Add 85 to parent page INSERT(87)
  • 72. https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 85 69 70 78 val val val 85 86 87 val val val 90 Add 85 to parent page What if the parent page is full? Split it INSERT(87)
  • 73. https://carlosproal.com/ir/papers/p121-comer.pdf 60 69 85 69 70 78 val val val 85 86 87 val val val 90 Add 85 to parent page How does update work? 1. Find the leaf page with key 2. Edit the row 3. Overwrite the page INSERT(87)
  • 74. LSM trees Vs B-Trees storage engine LSM Tree B-Tree Optimized for write Optimized for read Compressed better(No Fragmentation) Fragmentation wastes space There can be duplicates before compaction Each key exist exactly in one place Strong transaction support Spikes in write can cause slow compaction due to many SSTable files. Can cause Out of Memory Error(OOM)
  • 75. Space optimization in B-tree Primary index(primary key index) Secondary index
  • 76. Space optimization in B-tree Secondary index Primary index(primary key index) Leaf page contains both key and value Leaf page contains both key and value DUPLICATE !
  • 77. Space optimization in B-tree Secondary index Primary index(primary key index) Store value offset(smaller in size) Store value offset (smaller in size) val1 val2 val3 val4 val5 … Heap File
  • 78. Space optimization in B-tree Secondary index Primary index(primary key index) Store value offset(smaller in size) Store value offset (smaller in size) val1 val2 val3 val4 val5 … Heap File Store value offset(smaller in size) Extra Disk I/O So you can store important columns in leaf page and less important columns in heap file