Databases — the Choice Is Yours

In hardly any other area we’ve had as much change and improvement as in the field of databases. Just a few years ago everyone had “their” database that was used for each project. Today you are confronted with a variety of approaches and implementations.

We start off with a brief look at the theoretical background of distributed systems and databases in particular. After that, we discuss possible scenarios as well as the advantages and disadvantages of several databases:

* Why SQL is in fashion (again).
* Why MongoDB’s document structure fits object-oriented programming so well.
* How you can capture visitor hits with Redis efficiently.
* Why Cassandra is so scalable and fail-safe.
* How full-text search works with Elasticsearch.

The right choice of database(s) hasn’t become easier through the wide range of possibilities, but all the more interesting!

Transcript

1. Databases the Choice Is Yours Philipp Krenn̴̴̴̴̴̴̴@xeraa
5. Infrastructure | Developer Advocate
6. ViennaDB Papers We Love Vienna
9. "relational model and normal form" ↓ "data independence"
10. Relational Algebra Book "Seven Databases in Seven Weeks"
12. Declarative Implementation detail of the RDBMS (Query Optimizer)
13. User Interaction Aggregated reports Concurrency, integrity, consistency, type-safety
14. Codd's 12 rules
16. From a Wide Variety of Databases to A Dark Age
18. http://blog.8thlight.com/uncle-bob/ 2012/05/15/NODB.html
19. Slow & inflexible
23. https://aphyr.com/posts/288-the- network-is-reliable
24. Beating the CAP Theorem Checklist Your ( ) tweet ( ) blog post ( ) marketing material ( ) online comment advocates a way to beat the CAP theorem. Your idea will not work. http://ferd.ca/beating-the-cap-theorem-checklist.html
25. Schema Flexibility http://pragprog.com/magazines/ 2012-05/beyond-the-bit-bucket
30. NoSQL Not Only SQL
33. Big Data Very broad, including NoSQL Offline data / data warehouse, Hadoop & Spark
35. http://db-engines.com/en/ranking
37. CouchDB, Couchbase, Elasticsearch,...
38. Ted Neward: ORM is "The Vietnam of Computer Science" http://blogs.tedneward.com/post/the-vietnam-of-computer- science/
39. /** * @ManyToMany(targetEntity="Phonenumber") * @JoinTable(name="users_phonenumbers", * joinColumns={@JoinColumn(name="user_id", referencedColumnName="id")}, * inverseJoinColumns={@JoinColumn(name="phonenumber_id", * referencedColumnName="id", unique=true) * } * ) */ public $phonenumbers;
41. abstract class Person { protected $name; } class Employee extends Person { protected $company; } class Manager extends Employee { protected $approveFunds; } class Worker extends Employee { protected $yearsExperience; }
42. RDBMS 1. Union table with (many) NULL values
43. RDBMS 2. Concrete instances without common queries
44. RDBMS 3. Base table JOINed with concrete instances
45. /** * @MappedSuperclass */ abstract class Person { /** @Id */ protected $id; /** @String */ protected $name; }
46. /** * @Document(collection="person") * @InheritanceType("SINGLE_COLLECTION") * @DiscriminatorField(fieldName="type") * @DiscriminatorMap({ * "person"="Person", * "employee"="Employee", * "worker"="Worker" * }) */ class Employee extends Person { /** @String */ protected $company; }
47. /** @Document */ class Manager extends Employee { /** @Boolean */ protected $approveFunds; } /** @Document */ class Worker extends Employee { /** @Integer */ protected $yearsExperience; }
48. { "_id": ObjectId("5461c8bf9e2acf32ed50c079"), "name": "Peter", "company": "MongoDB", "approveFunds": true, "type": "manager" } { "_id": ObjectId("524d9fe7e4b0f8bd3031f84e"), "name": "Philipp", "company": "MongoDB", "yearsExperience": 10, "type": "worker" }
51. Key-Value Store and more REmote DIctionary Server
52. Statistics (Login) Bitset HyperLogLog
53. Bitset First user bit 1, second user bit 2,... A million users ~123KB
54. HyperLogLog Unique elements in 12KB regardless of their number Error rate ~0,81% http://redis.io/commands/pfcount
55. Comparison HyperLogLog: constant size Bitset: exact & aggregates
56. Overall Simple Omni-present "glue"
58. Scalable & highly available Consistent Hashing
64. * Without read and write QUORUM
65. Eventually consistent was too hard, so we went with immediately inaccurate. — https://twitter.com/mysqlborat/status/621785755107524608
68. SQL Databases not only RDBMS
69. Cassandra Query Language (CQL) Google Query Language (GQL) Couchbase N1QL RethinkDB Query Language (ReQL)
70. RDBMS features & maturity + NoSQL influence
72. Full-Text Search
74. Example These are <em>not</em> the droids you are looking for.
75. html_strip Char Filter These are not the droids you are looking for.
76. standard Tokenizer These̴are̴not̴the̴droids̴you̴are̴looking̴for
77. lowercase Token Filter these̴are̴not̴the̴droids̴you̴are̴looking̴for
78. stop Token Filter droids̴you̴looking
79. snowball Token Filter droid̴you̴look
80. Document Score Term Frequency (TF) / Inverse Document Frequency (IDF) Field-length norm https://www.elastic.co/guide/en/elasticsearch/guide/current/scoring-theory.html
81. Conclusion
83. But is it fast?
86. Thanks! Questions? Stickers @xeraa̴̴https://joind.in/talk/ d897d
87. Credits Schnitzel https://flic.kr/p/9m27wm Architecture https://flic.kr/p/6dwCAe Conchita https://flic.kr/p/nBqSHT Consistent hashing https:// highlyscalable.wordpress.com/2012/09/18/ distributed-algorithms-in-nosql-databases/ No, SQL https://twitter.com/edd/status/ 400190499585544192