See also:

  • Datalog
  • concurrency

Key Value Store

log structured storage a log is a append only store LSM - log structured merge trees. In memory table for writes. Flushed to disk. Multiple read only written to disk, coalesced in background. sstable Tombstone records for deletes. https://www.igvita.com/2012/02/06/sstable-and-log-structured-storage-leveldb/

What’s the big deal about key-value databases like FoundationDB and RocksDB? lobster comments https://lobste.rs/s/avljlh/what_s_big_deal_about_embedded_key_value#c_rx0oid

wide-column store key/value store

Embedded key value store. Backing engines. MySql has support for many backing engines

Algorithms

B-trees

OLTP online transaction processing OLAP online analytical processing hyperloglog bloom filters cuckoo filter

Theory

Conjunctive Queries

Query containment

  • See finite model theory

descriptive complexity NC^0 bounded fan in AC^0 https://en.wikipedia.org/wiki/AC0 unbounded fan in circuit. Constant height https://en.wikipedia.org/wiki/Circuit_complexity

https://pages.cs.wisc.edu/~paris/cs838-s16/lecture-notes/lecture1.pdf

Foundations of database

Schema

https://en.wikipedia.org/wiki/Database_normalization

schema is finite set of relation symbol names an instance is a set of concrete relations with those symbol names. Sometimes also called a structure

Functional Dependencies

Armstrong axioms

Normal Formals

Tuple Generating dependencies

Query Optimization

Cascades framework https://github.com/egraphs-good/egg/discussions/189

Zetasql calcite

The Chase

Equality Generating Dependencies The Chase Procedure and its Applications in Data Exchange

Yisu: query optimization data integration querying incomplete databases benchmarking the chase chasebench

Chasefun, DEMOo, Graal, llunatic, pdg, pegasus, dlv, e, rdfox

Stratgeies - (restricted, unrestricted, parallel, skolem, fresh-null

Chase Strategies vs SIPS

The power of the terminating chase

Is the chase meant to be applied to actual databases, symbolic databases / schema, or other dependencies? Is it fair the say that the restricted chase for full dependencies is datalog?

Alice book chapter 8-11

Graal - https://github.com/hamhec/DEFT https://hamhec.github.io/DEFT/ defeasible programming http://lidia.cs.uns.edu.ar/delp_client/ Something about extra negation power? Defeatable rules if something contradicts them Pure is part of graal

llunatic - https://github.com/donatellosantoro/Llunatic

RDfox - https://docs.oxfordsemantic.tech/

dlgp - datalog plus format. Allows variables in head = existentials. Variables in facts. Notion of constraint ! :- and notion of query. Hmm.

Direct modelling of union find in z3? homomorphism is union find

SQL

sql injection https://ctf101.org/web-exploitation/sql-injection/what-is-sql-injection/ everything is foreign keys? Interning

Recursive tables let you do datalog like stuff.

CREATE TABLE edge(a INTEGER, b INTEGER);
INSERT INTO edge(a,b)
VALUES
    (1,2),
    (2,3),
    (3,4);
SELECT a,b FROM edge;

CREATE TABLE path(a INTEGER, b INTEGER);
--INSERT INTO path
--SELECT * FROM edge;

-- path(x,z) :- edge(x,y), path(y,z).
WITH RECURSIVE
  path0(x,y) AS
    -- SELECT 1,2
    (SELECT a,b FROM edge UNION SELECT edge.a, path0.y FROM edge, path0 WHERE path0.x = edge.b )
  INSERT INTO path SELECT x,y FROM path0;
      
SELECT a,b FROM path;
.quit

UF

WITH RECURSIVE 
  parent(x,y) AS
  SELECT a, min(b) (SELECT (a,b) FROM eq UNION eq, parent)

python sqlite3 in stdlib

import sqlite3
con = sqlite3.connect(':memory:')
cur = con.cursor()
# Create table
cur.execute('''CREATE TABLE stocks
               (date text, trans text, symbol text, qty real, price real)''')

# Insert a row of data
cur.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")

#cur.executemany("insert into characters(c) values (?)", theIter)
for row in cur.execute('SELECT * FROM stocks ORDER BY price'):
  print(row)

adapters to python types https://en.wikipedia.org/wiki/Materialized_view

sqlite loadable extensions



indices

views

Saved queries that act as virtual tables

triggers

This is interesting

Aggregate functions

Window Functions

Ontology Formats

graph database OWL RDF sparql sparql slides shacl -

semantic web

Knowdlege representation handbook Course https://web.stanford.edu/class/cs227/Lectures/lec02.pdf very similar to bap knoweldge base

Optimal Joins

worst case optimal join algorithm leapfrog triejoin https://github.com/frankmcsherry/blog/blob/master/posts/2018-05-19.md Dovetail join - relational ai unpublished. Julia specific ish? https://relational.ai/blog/dovetail-join use sparsity of all relations to narrow down search Worst case optiomal join Ngo pods 2012 leapfrog triejoin simpel worst case icdt 2015 worst case optimal join for sparql worst case optimal graph joins in almost no space Correlated subqueries: unnesting arbitrary queries How materializr and other databases optimize sql subqueries

genlteish intro to worst case optimal joins

Adopting Worst-Case Optimal Joins in Relational Database Systems tries The Adaptive Radix Tree: ARTful Indexing for Main-Memory Databases Persistent Storage of Adaptive Radix Trees in DuckDB

oltp indices 2

umbra spiritual successor to hyper. Hybridizes an in memory system to also work off ssd.

Vectorized Execution

cmu adavanced course lecture Rethinking SIMD Vectorization for In-Memory Databases

masked/selective load masked/selective store scatter gather

selection: branched vs branchless branched checks condition to see if should copy row out branchless writes but only increments index of storage by one if condition is met. I mean. There is a “branch” in this. But I see your point

EmptyHeaded: A Relational Engine for Graph Processing “generalized hypertree decomposition” ? https://github.com/HazyResearch/EmptyHeaded

levelheaded linear algerba stuff?

Multi Version Concurrency Control

https://en.wikipedia.org/wiki/Multiversion_concurrency_control

Duckdb

https://duckdb.org/ sqlite for olap columnar

import duckdb
con = duckdb.connect(database=':memory:')
import pandas as pd
test_df = pd.DataFrame.from_dict({"i":[1, 2, 3, 4], "j":["one", "two", "three", "four"]})
con.execute('SELECT * FROM test_df')
#print(con.fetchall())
print(con.fetchdf())

add_df = pd.DataFrame(columns=["x","y","z"])
print(add_df)
counter = 0
def add(x,y):
  global counter, add_df
  cond = add_df["x"] == x #& add_df["y"] == y
  df = add_df[cond]
  if not df.empty:
    return add_df["z"][0]
  else:
    z = counter
    add_df.append((x,y,z))
    counter += 1
    return z

print(add(-1,-2))

catalog multiversion concrruncy control cimpressed execution binder

Relational AI

https://www.youtube.com/watch?v=WRHy7M30mM4&ab_channel=CMUDatabaseGroup

snowflake databricks bigquery dbt fivetran

data apps - dapps

lookml sigma legend

Resposnive compilter - matsakis salsa.jl umbra/leanstore

incremental COnvergence of datalog over presmeirings differential dataflor cidr2013 reconciling idfferences 2011 Green F-IVM incrmenetal view mantinance with triple lock fotrization benefits

systemml vecame apache systemds https://systemds.apache.org/

Semantic optimization FAW question asked frequence : Ngo Rudra PODS 2016 What do shannon type ineuqlaities submodular width and disjunctive datalog have to do with one another pods 2017 precise complexity analysis for efficient datalog queries ppdp 2010 functional aggregate queries with additive inequalities convergence of dtalog over pr-esemirign

Relational machine learning Layered aggregate engine for analystics worloads schelich olteanu khamis leanring models over relational data using sparse tenosrs The relational data borg is learning olteanu vldb keynote sturcture aware machine learning over multi relational database relational know graphs as the ofundation for artifical intelligence km-means: fast clustering for relational data https://arxiv.org/abs/1911.06577 Learning Models over Relational Data: A Brief Tutorial

duckdb for sql support calcite postgresql parser

Fortress library traits. OPtimization and parallelism https://relational.ai/blog/categories/research

https://arxiv.org/abs/2004.03716 triangle view mantenance

Streaming

streaming 101 unbounded data

https://en.wikipedia.org/wiki/Stream_processing

lambda architecture - low latency inaccurate, then batch provides accurate

event time vs processing time

Watermark

Flink Apache Beam millwheel spark streaming

https://materialize.com/blog

Data Structures

B Tree

Bw-tree The B-Tree, LSM-Tree, and the Bw-Tree in Between open bw-tree 2018

Radix Trie

CRDTs

Conflict Free replicated datatypes https://crdt.tech/ martin Kleppmann

CRDT of string - consider fractional positions. Tie breaking. Bad interleaving problem unique identifiers

  • LSeq
  • RGA
  • TreeSeq

https://www.inkandswitch.com/peritext/ crdt rich text

https://github.com/josephg/diamond-types https://josephg.com/blog/crdts-go-brrr/

https://github.com/yjs/yjs

automerge: library of data structures for collab applications in javascript https://mobiuk.org/abstract/S4-P5-Kleppmann-Automerge.pdf local first. use local persistent storage. git for your app’s data. rust implementation?

isabelle crdt I was wrong. CRDTs are the future

Conflict-free Replicated Data Types” “A comprehensive study of Convergent and Commutative Replicated Data Types

Operational Transformation - sequences of insert and delete. Moves possibly.

delta-based vs state-based https://bartoszsypytkowski.com/the-state-of-a-state-based-crdts/

counters

json crdt for vibes patches?

Tree move op. Create delete subtrees.

Synthesizing CRDTs from Sequential Data Types with Verified Lifting https://arxiv.org/abs/2205.12425

Big Data

Spark Hadoop MapReduce Dask Flink Storm

Mahout Vowpal Wabbit

hadboop

Giraph

Spark

https://en.wikipedia.org/wiki/Apache_Spark Databricks - company bigdatalog https://www.cis.upenn.edu/~susan/cis700/Papers/BigDataAnalyticsSPARK.pdf https://github.com/ashkapsky/BigDatalog MLlib spark streaming graphx

Message brokrs

RabbitMQ Kafka

Services

BigQuery Snowflake Azure AWS

Graph systems

It isn’t that relational systems can’t express graph problems. But maybe graph systems are more optimized for the problem neo4j Giraph Powergraph graphrex graphx myria graphchi xsteam gridgraph graphlab

SQL

  • create table
  • create index
  • explain query plan I saw explain analyze elsewhere
  • select
  • vacuum - defrag and gabrage collect the db
  • begin transaction

    sqlite

    sqlite commands that are interesting

  • .help
  • .dump
  • .tables
  • .schema
  • .indexes
  • .expert suggests indices?

Resources

Conferences

duckdb embedded like sqlite?

https://xtdb.com/

Conjunctive-query containment and constraint satisfaction

Designing Data intensive systems martin kleppmann

scalability but at what cost? big systems vs laptops.

Data integration the relational logic approach

postgres indexes for newbies postgres tutorial raytracer in sql [advent of code sql(https://news.ycombinator.com/item?id=29467671)] sqllancer detecting lgoic bugs in dbms

  • Differential Datalog
  • CRDTs
  • Differential Dataflow
  • Nyberg Accumulators
  • Verkle Trees
  • Cryptrees
  • Byzantine Eventual Consistency
  • Self-renewable hash chains
  • Binary pebbling

https://github.com/dbuenzli/rel

Ezra Cooper. The Script-Writer’s Dream: How to Write Great SQL in Your Own Language, and Be Sure It Will Succeed. 2009. Full text

James Cheney et al. A practical theory of language-integrated query. 2013. Full text

Suzuki et al. Finally, safely-extensible and efficient language-integrated query. 2016. Full text

Oleg Kiselyov et al. Sound and Efficient Language-Integrated Query – Maintaining the ORDER. 2017. Full text

DBSP: Automatic Incremental View Maintenance for Rich Query Languages - mcsherry et al

pavlo advanced databases

awesome database learning

database architects blogs

https://www.reddit.com/r/databasedevelopment/

https://twitter.com/phil_eaton

database internals

Ask HN: What could a modern database do that PostgreSQL and MySQL can’t

postgres internals book