Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie OldSQL to NewSQL
Ähnlich wie OldSQL to NewSQL (20)
Mehr von Claus Matzinger
Mehr von Claus Matzinger (7)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
OldSQL to NewSQL
- 1. CrateDB & PostgreSQL OldSQL to NewSQL 11th July 2017 @claus__m
- 2. About ~2yrs at Crate.io DevRel/Field Engineering/Support/ Integrations/… Speaking Conferences, meetups, ... Working with customers Consulting, pre- and post-sales @claus__m
- 3. Agenda Failures What, how, and when? PostgreSQL Concept overview CrateDB Concept overview Discussion NewSQL or not? Benefits and drawbacks. Use Cases Wrap up @claus__m
- 6. Database Failures Consequences Data loss Lost updates, dirty reads, ... Service interruptions Services can’t work without their database Slow performance Users may lose interest Pressure DBAs in the spotlight @claus__m
- 7. What Makes Databases Fail? Overloaded Insufficient hardware (RAM, CPU, disk), swapping, inefficient queries Failure Hardware may fail on many levels: e.g. Network, disk, RAM Platform Configuration errors, updates, resource sharing, bugs People Malicious intent, sloppiness, ... @claus__m
- 8. Overloaded Insufficient hardware (RAM, CPU, disk), swapping, inefficient queries Failure Hardware may fail on many levels: e.g. Network, disk, RAM Platform Configuration errors, updates, resource sharing, bugs People Malicious intent, sloppiness, ... @claus__m What Makes Databases Fail?
- 9. Overview Concepts and other things Index and data How the database creates indices, stores and retrieves data Search and scans How the data is found Replication and high availability Distribution and achieving zero downtime @claus__m Assessment
- 11. Overview Multi-process System fork() to clone processes from postmaster to postgres instances with shared memory Technology C/C++ based natively compiled Optimization Cost-based optimizer Transactional ACID compliant @claus__m
- 12. Index And Data Tree-based An in-memory B-Tree, defined in CREATE TABLE or ALTER TABLE In Memory & On Disk 8K data pages in shared buffer cache and on disk Item Pointers Only major changes are reflected in the index (e.g. INSERT/DELETES) @claus__m
- 14. Searches And Scans Sequential Go over every block and execute a predicate Index-based Find something using an index on that column, or a full index scan Bitmap-based Mark matches in boolean queries for results @claus__m
- 15. Replication And High Availability Disk based By sharing a disk or continuously cloning a disk Log-shipping Send the write-ahead-log to the standby server, which can answer reads Master/Master Sends rows to the other master, can answer reads and writes, locks rows/tables Client-sharding Shard the data on a client/proxy and route accordingly @claus__m
- 17. Overview Multi-threaded System Thread-pools to read/write Lucene segments Technology Java/JVM based Optimization Naive optimization on query levels Eventually Consistent Atomic operations per row, optimistic concurrency only Distributed By Default Transparent partitioning and sharding @claus__m
- 18. Index And Data Inverted index Term dictionary where field values point to rows (posting list) Field cache “Inverted inverted index”, column names point to the possible values and their rows On disk, cached in memory Immutable segments on disk, binary search in each segment, cached with mmap() into ram pages @claus__m
- 20. Index And Data @claus__m Shards Compounds of multiple immutable segments, merged occasionally Rows are documents, columns are fields Vector space model to weight and score searches (_score field) Multi-threaded index access Shards are multiple segments, each is read with a thread
- 21. Replication And High Availability Shared nothing architecture Every node handles every task Shard-based Replicas are copies of shards that are distributed in the cluster evenly Consistency Elected leader maintains and distributes a consistent cluster state CAP Tuneable consistency with synchronous inserts @claus__m
- 23. PostgreSQL: Strengths Single-Node-Performance Predictable and fast SQL Sophistication Lots of features, many of them heavily optimized Transactions ACID compliance, concurrency control @claus__m
- 24. PostgreSQL: Weaknesses Distribution High availability or working with huge data sets requires 3rd party software, partitioning Ingest speed ACID compliance slows down inserts Operational Complexity/DevOps Readiness Highly controllable features make it hard to manage Schema Flexibility Schema evolution management required @claus__m
- 25. CrateDB: Strengths Distribution Distributed by nature, with tunable consistency Ingest speed Solid insert speeds with bulk inserts Operational Complexity/DevOps Readiness High flexibility, containerization, sane defaults Schema Flexibility Schema evolution on the fly Built-in Search Fulltext capabilities @claus__m
- 26. CrateDB: Weaknesses Single-Node-Performance Distribution overhead requires a certain cluster size to be efficient SQL Features Many features are yet missing or hard to do in a distributed system Transactions No ACID compliance, eventual consistency/optimistic concurrency requires client-side handling @claus__m
- 28. Use Cases: PostgreSQL ORMs Broad integration in various object-relational mappers in frameworks (hibernate, …) Transaction-based workloads Single, high-value transactions Extensive SQL compliance Required support for views, stored procedures, … Small data sets Hundreds of MBs to several GB @claus__m
- 29. Use Cases: CrateDB DevOps Flexible schemas, ad-hoc queries, easy maintenance Analytics, machine learning Large scale inserts/queries, high concurrency, SQL Fulltext search Built-in tools for text-mining/analysis, built on the de-facto standard of search @claus__m
- 30. Thanks! Links https://github.com/crate https://crate.io Follow us on twitter @crateio @claus__m Next webinar: Scale your SQL database with Docker, 27th July
- 31. Q & A