Your Database Wants
to Kill You
Kevin Lawver - 11/1/2013

1
Hi, I’m Kevin.

2
3

-

I work at Rails Machine
We do ops
Lots of ops on lots of different kinds of databases
enough introductions, let’s ge...
Databases have been
around since before
most of us were born.

4

- So they’re well understood
- and well despised
- and c...
There’s been a
revolution the past few
years.

5
Getting away from fully
relational databases, to
something... odd.

6
But, don’t get
comfortable.

7
Because your database
wants...

8
TO KILL YOU!

9
really, it does.

10
The Old School

11
Relational Databases

12
MySQL, PostgreSQL,
Oracle, Sybase, etc

13
This is what you’re
used to.

14
Tables, relationships,
foreign keys, SQL, etc.

15

- And lots of rules
ACID

16

The set of rules relational databases follow to assure the data gets where it needs to go and
is consistent.
The...
Atomicity

17

Transactions are all or nothing. If any part of the transaction fails, the WHOLE thing has to
fail and roll...
Consistency

18

Any transaction brings the database from one valid “state” to another - which means you can
have a bunch ...
Isolation

19

Transactions executed concurrently have to result in the same state of the database as if they
had been exe...
Durability

20

Once a transaction is committed, it’s IN THERE.
That’s a lot of rules, and
it makes for inflexible
systems.

21
And that’s where the
killing comes in:

22
Replication

23

It’s evil, and almost all RDBMS’s do it wrong.
It’s so fragile that you spend more time redoing it than a...
Failover

24

This is even worse than replication. Because it was even more of an afterthought.
Most of the time it fails ...
All those solutions are
hacked on and horrible.

25
There has to be a
better way.

26
Enter the CAP
Theorem

27
It came from Amazon,
and changed everything.

28

It adds some reality to the database world. It basically says that no da...
CAP stands for...

29
Consistency

30

All nodes have the same data at the same time.
Availability

31

Every request is guaranteed to receive a response as to its success or failure
Partition Tolerance

32

The system will continue to operate despite arbitrary message loss or a failure of part of the
sy...
But, you can never have
all three. It’s impossible.

33

Finally, some reality! Stop trying to be everything to everyone a...
Enter all the NoSQL!

34

Stands for either “NO SQL” or “Not Only SQL” - but it’s really a bunch of different data stores
...
Document Stores

35

-

MongoDB, Riak, CouchDB, etc
Not relational (though you can convince mongodb to do it, you shouldn’...
MySQL Replication

36

That’s typical master/master.
Each can take writes (but you shouldn’t)
They ship bin logs back and ...
MongoDB Replica Set

37

- There’s an election, and one node is picked as the primary.
- It takes all writes, distributes ...
Benefits of Replica Sets

38

- Replication and failover designed into the system as core functionality!
- Much better fail...
Riak & the “Ring”

39



Riak is crazy town
Document store with very light querying (though the new search stuff is badass...
The Ring

40

-

All nodes “gossip” to confirm they’re up.
Any node can take a query and will gather the results from the o...
What’s Old is New

41

-

MariaDB + Galera Cluster = MySQL replica sets! (kind of)
row-based replication is much more reli...
My MariaDB

42

- Yes, this is the mongodb diagram
- I use haproxy to send all the writes to a single primary, with the ot...
Here’s HAProxy

43

-

rmcom_backend - app servers
mariadb_read_backend - the leastconn balanced pool of readers
mariadb_w...
Now, some rules...

44
If you query it, index it.

45

- As your data grows, you’ll see query speed decrease.
- Add indexes for your common queri...
As data increases,
flexibility decreases.

46

- You’ll need to limit the types of queries you allow people to perform beca...
Cache it!

47

- Use memcached or other caching technologies to keep common queries away from the
database.
- If it can be...
Scale vertically

48

- Throw hardware at it until it’s too expensive, then shard it.
- Because sharding is almost always ...
What does it all mean?

49

-

Don’t default to RDBMS!
Use RDBMS if you need transactions and your data truly is relationa...
If We Have Time...
•
•
•

Key/Value Stores

•

Questions!

Elasticsearch
Why you shouldn’t use
Redis... ever.

50
RailsBridge!
http://rubysavannah.com - 11/16/2013

51

- We need front-end volunteers and students!
- Next one is in Janua...
Thank you!
• kevin@railsmachine.com
• @kplawver
• http://railsmachine.com
• http://lawver.net
52
Upcoming SlideShare
Loading in...5
×

Your Database is Trying to Kill You

151

Published on

A presentation I gave to a bunch of college kids all about databases, why they're horrible, and what's coming. I also threw in some rules to make their lives a little easier, because I'm not entirely cruel.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
151
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Your Database is Trying to Kill You"

  1. 1. Your Database Wants to Kill You Kevin Lawver - 11/1/2013 1
  2. 2. Hi, I’m Kevin. 2
  3. 3. 3 - I work at Rails Machine We do ops Lots of ops on lots of different kinds of databases enough introductions, let’s get w/ the murder!
  4. 4. Databases have been around since before most of us were born. 4 - So they’re well understood - and well despised - and crusty
  5. 5. There’s been a revolution the past few years. 5
  6. 6. Getting away from fully relational databases, to something... odd. 6
  7. 7. But, don’t get comfortable. 7
  8. 8. Because your database wants... 8
  9. 9. TO KILL YOU! 9
  10. 10. really, it does. 10
  11. 11. The Old School 11
  12. 12. Relational Databases 12
  13. 13. MySQL, PostgreSQL, Oracle, Sybase, etc 13
  14. 14. This is what you’re used to. 14
  15. 15. Tables, relationships, foreign keys, SQL, etc. 15 - And lots of rules
  16. 16. ACID 16 The set of rules relational databases follow to assure the data gets where it needs to go and is consistent. They’re fine for a certain kind of workload.
  17. 17. Atomicity 17 Transactions are all or nothing. If any part of the transaction fails, the WHOLE thing has to fail and roll back. That means a lot of locking, which can become a performance problem.
  18. 18. Consistency 18 Any transaction brings the database from one valid “state” to another - which means you can have a bunch of rules inside the database to judge the validity of data, and any transaction that doesn’t pass fails and rolls back. Again, not great for performance.
  19. 19. Isolation 19 Transactions executed concurrently have to result in the same state of the database as if they had been executed serially. Requires partially applied transactions to NOT be visible to other transactions.
  20. 20. Durability 20 Once a transaction is committed, it’s IN THERE.
  21. 21. That’s a lot of rules, and it makes for inflexible systems. 21
  22. 22. And that’s where the killing comes in: 22
  23. 23. Replication 23 It’s evil, and almost all RDBMS’s do it wrong. It’s so fragile that you spend more time redoing it than actually getting any benefit from it. MySQL can do master/master. PostgreSQL ships binary logs via scp. It’s all horrible and gives me grey hairs. Because it was an afterthought and not designed from the beginning. Add-on replication is almost always horrible.
  24. 24. Failover 24 This is even worse than replication. Because it was even more of an afterthought. Most of the time it fails over on accident and breaks replication. And then someone gets woken up to clean up a steaming pile of bad data. And that person isn’t very happy about it.
  25. 25. All those solutions are hacked on and horrible. 25
  26. 26. There has to be a better way. 26
  27. 27. Enter the CAP Theorem 27
  28. 28. It came from Amazon, and changed everything. 28 It adds some reality to the database world. It basically says that no database can do everything.
  29. 29. CAP stands for... 29
  30. 30. Consistency 30 All nodes have the same data at the same time.
  31. 31. Availability 31 Every request is guaranteed to receive a response as to its success or failure
  32. 32. Partition Tolerance 32 The system will continue to operate despite arbitrary message loss or a failure of part of the system. Also known as “split brain” - which happens to me if I don’t get enough coffee.
  33. 33. But, you can never have all three. It’s impossible. 33 Finally, some reality! Stop trying to be everything to everyone and solve all types of problems with the same hammer. So when you’re looking at a data store, see which two it can do and which you need for your data!
  34. 34. Enter all the NoSQL! 34 Stands for either “NO SQL” or “Not Only SQL” - but it’s really a bunch of different data stores that aren’t relational and solve different kinds of problems. And provide some solutions for old school reliability problems.
  35. 35. Document Stores 35 - MongoDB, Riak, CouchDB, etc Not relational (though you can convince mongodb to do it, you shouldn’t) Usually have really good replication stories Let’s look at MongoDB vs traditional MySQL
  36. 36. MySQL Replication 36 That’s typical master/master. Each can take writes (but you shouldn’t) They ship bin logs back and forth Fragile Easy to break replication by having conflicting writes committed near the same time on both sides - so split-brain is always a possibility.
  37. 37. MongoDB Replica Set 37 - There’s an election, and one node is picked as the primary. - It takes all writes, distributes to the secondaries - If the primary goes down, there’s an election and a new primary is chosen (usually less than 1 second). - New nodes join the replica set and get all the data, then can be elected primary
  38. 38. Benefits of Replica Sets 38 - Replication and failover designed into the system as core functionality! - Much better failover - Much better reliability - I get to sleep more - Easy to add capacity as the replica set grows (either shard by adding new replica sets or add more nodes to scale reads).
  39. 39. Riak & the “Ring” 39 Riak is crazy town Document store with very light querying (though the new search stuff is badass) Super scalable via the “Ring” Data is automagically replicated around the ring based on configuration - Number of copies
  40. 40. The Ring 40 - All nodes “gossip” to confirm they’re up. Any node can take a query and will gather the results from the other nodes. Nodes dropping out are “noticed” by the ring and data gets shuffled around. New news automatically join the node and get their “share” of the data. Theoretically infinitely scalable (though the gossip gets REALLY noisy) Useful as a file store (see Riak CS) I think that drawing can be used to summon Beetlejuice.
  41. 41. What’s Old is New 41 - MariaDB + Galera Cluster = MySQL replica sets! (kind of) row-based replication is much more reliable automatic failover and syncing of new nodes can be load balanced for reads and writes! still the same sql everyone’s used to theoretically any node can take writes - but I don’t trust it
  42. 42. My MariaDB 42 - Yes, this is the mongodb diagram - I use haproxy to send all the writes to a single primary, with the others as backups in case it goes down. - I have a separate haproxy frontend that load balances across all three for reads. - so far, i love it to pieces
  43. 43. Here’s HAProxy 43 - rmcom_backend - app servers mariadb_read_backend - the leastconn balanced pool of readers mariadb_write_backend - db1 is the primary unless it goes down, then db2 is “promoted” rails, mariadb_read and mariadb_write are the frontends
  44. 44. Now, some rules... 44
  45. 45. If you query it, index it. 45 - As your data grows, you’ll see query speed decrease. - Add indexes for your common queries! - Don’t forget compound indexes.
  46. 46. As data increases, flexibility decreases. 46 - You’ll need to limit the types of queries you allow people to perform because they’ll lock things up and stop everyone from accessing it. - You’ll need to find other ways to “protect” the database, like.
  47. 47. Cache it! 47 - Use memcached or other caching technologies to keep common queries away from the database. - If it can be read, it can be cached. - Saves you a ton of money in vertically scaling your database. - You may also need to add other ways to access your data, like say, elasticsearch or solr.
  48. 48. Scale vertically 48 - Throw hardware at it until it’s too expensive, then shard it. - Because sharding is almost always horrible.
  49. 49. What does it all mean? 49 - Don’t default to RDBMS! Use RDBMS if you need transactions and your data truly is relational. If it’s a document, use a document store Understand the tradeoffs Understand how your data will be queried Don’t forget you can combine technologies to build whatever you need
  50. 50. If We Have Time... • • • Key/Value Stores • Questions! Elasticsearch Why you shouldn’t use Redis... ever. 50
  51. 51. RailsBridge! http://rubysavannah.com - 11/16/2013 51 - We need front-end volunteers and students! - Next one is in January so check back in November for the signup!
  52. 52. Thank you! • kevin@railsmachine.com • @kplawver • http://railsmachine.com • http://lawver.net 52

×