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1 [Note: this is the Redis manifesto, for general information about
2 installing and running Redis read the README file instead.]
4 Redis Manifesto
7 1 - A DSL for Abstract Data Types. Redis is a DSL (Domain Specific Language)
8 that manipulates abstract data types and implemented as a TCP daemon.
9 Commands manipulate a key space where keys are binary-safe strings and
10 values are different kinds of abstract data types. Every data type
11 represents an abstract version of a fundamental data structure. For instance
12 Redis Lists are an abstract representation of linked lists. In Redis, the
13 essence of a data type isn't just the kind of operations that the data types
14 support, but also the space and time complexity of the data type and the
15 operations performed upon it.
17 2 - Memory storage is #1. The Redis data set, composed of defined key-value
18 pairs, is primarily stored in the computer's memory. The amount of memory in
19 all kinds of computers, including entry-level servers, is increasing
20 significantly each year. Memory is fast, and allows Redis to have very
21 predictable performance. Datasets composed of 10k or 40 millions keys will
22 perform similarly. Complex data types like Redis Sorted Sets are easy to
23 implement and manipulate in memory with good performance, making Redis very
24 simple. Redis will continue to explore alternative options (where data can
25 be optionally stored on disk, say) but the main goal of the project remains
26 the development of an in-memory database.
28 3 - Fundamental data structures for a fundamental API. The Redis API is a direct
29 consequence of fundamental data structures. APIs can often be arbitrary but
30 not an API that resembles the nature of fundamental data structures. If we
31 ever meet intelligent life forms from another part of the universe, they'll
32 likely know, understand and recognize the same basic data structures we have
33 in our computer science books. Redis will avoid intermediate layers in API,
34 so that the complexity is obvious and more complex operations can be
35 performed as the sum of the basic operations.
37 4 - We believe in code efficiency. Computers get faster and faster, yet we
38 believe that abusing computing capabilities is not wise: the amount of
39 operations you can do for a given amount of energy remains anyway a
40 significant parameter: it allows to do more with less computers and, at
41 the same time, having a smaller environmental impact. Similarly Redis is
42 able to "scale down" to smaller devices. It is perfectly usable in a
43 Raspberry Pi and other small ARM based computers. Faster code having
44 just the layers of abstractions that are really needed will also result,
45 often, in more predictable performances. We think likewise about memory
46 usage, one of the fundamental goals of the Redis project is to
47 incrementally build more and more memory efficient data structures, so that
48 problems that were not approachable in RAM in the past will be perfectly
49 fine to handle in the future.
51 5 - Code is like a poem; it's not just something we write to reach some
52 practical result. Sometimes people that are far from the Redis philosophy
53 suggest using other code written by other authors (frequently in other
54 languages) in order to implement something Redis currently lacks. But to us
55 this is like if Shakespeare decided to end Enrico IV using the Paradiso from
56 the Divina Commedia. Is using any external code a bad idea? Not at all. Like
57 in "One Thousand and One Nights" smaller self contained stories are embedded
58 in a bigger story, we'll be happy to use beautiful self contained libraries
59 when needed. At the same time, when writing the Redis story we're trying to
60 write smaller stories that will fit in to other code.
62 6 - We're against complexity. We believe designing systems is a fight against
63 complexity. We'll accept to fight the complexity when it's worthwhile but
64 we'll try hard to recognize when a small feature is not worth 1000s of lines
65 of code. Most of the time the best way to fight complexity is by not
66 creating it at all. Complexity is also a form of lock-in: code that is
67 very hard to understand cannot be modified by users in an independent way
68 regardless of the license. One of the main Redis goals is to remain
69 understandable, enough for a single programmer to have a clear idea of how
70 it works in detail just reading the source code for a couple of weeks.
72 7 - Threading is not a silver bullet. Instead of making Redis threaded we
73 believe on the idea of an efficient (mostly) single threaded Redis core.
74 Multiple of such cores, that may run in the same computer or may run
75 in multiple computers, are abstracted away as a single big system by
76 higher order protocols and features: Redis Cluster and the upcoming
77 Redis Proxy are our main goals. A shared nothing approach is not just
78 much simpler (see the previous point in this document), is also optimal
79 in NUMA systems. In the specific case of Redis it allows for each instance
80 to have a more limited amount of data, making the Redis persist-by-fork
81 approach more sounding. In the future we may explore parallelism only for
82 I/O, which is the low hanging fruit: minimal complexity could provide an
83 improved single process experience.
85 8 - Two levels of API. The Redis API has two levels: 1) a subset of the API fits
86 naturally into a distributed version of Redis and 2) a more complex API that
87 supports multi-key operations. Both are useful if used judiciously but
88 there's no way to make the more complex multi-keys API distributed in an
89 opaque way without violating our other principles. We don't want to provide
90 the illusion of something that will work magically when actually it can't in
91 all cases. Instead we'll provide commands to quickly migrate keys from one
92 instance to another to perform multi-key operations and expose the
93 trade-offs to the user.
95 9 - We optimize for joy. We believe writing code is a lot of hard work, and the
96 only way it can be worth is by enjoying it. When there is no longer joy in
97 writing code, the best thing to do is stop. To prevent this, we'll avoid
98 taking paths that will make Redis less of a joy to develop.
100 10 - All the above points are put together in what we call opportunistic
101 programming: trying to get the most for the user with minimal increases
102 in complexity (hanging fruits). Solve 95% of the problem with 5% of the
103 code when it is acceptable. Avoid a fixed schedule but follow the flow of
104 user requests, inspiration, Redis internal readiness for certain features
105 (sometimes many past changes reach a critical point making a previously
106 complex feature very easy to obtain).