Version 1.0, 1 Feb 2017: Intial specification. Version 1.1, 2 Feb 2017: Integer encoding simplified. Appendix A added. Version 1.2, 3 Feb 2017: Better specify the meaning of the num-elements field with value of 65535. The two 12 bits positive/negative integers encodings were replaced by a single 13 bit signed integer. Crash resistance better specified. (Thanks to Oran Agra for all the hints). Salvatore Sanfilippo Yuval Inbar Oran Agra
Since the early stage of Redis development, to optimize for low memory usage was an important concern. Scalable data structures are often composed of nodes (heap allocated chunks of memory) containing references (pointers) to other nodes. This representation, while able to scale well with the number of elements in a data structure, is extremely wasteful: meta data easily account for 50% of the space in memory if the average element size is small. However when a data structure is used to hold a very small number of elements, it is possible to switch to a different, more compact representation. Because the number of elements for which the alternating representation is used is constant and small, the time complexity of the data structure is the same. Moreover, the constant times of working with such compact representation of a small number of elements, even when a full scan of the elements is needed in order to access or modify the data structure, are well compensated by the cache locality of sequentially accessing a linear array of bytes. This allows to save memory, while transparently switching to a linked representation once a given maximum size is reached.
Traditionally Redis, as compact representation of hashes, lists, and sorted sets
having few elements, used a data structure called ziplist. A ziplist is
basically a single heap allocated chunk of memory containing a list of string
elements. It can be used to represent maps by alternating keys and values, or
ordered lists of elements. The ziplist data structure served us very well for
years, however recently an user signaled a crash in the context of accessing
ziplists. The bug happened with error corrected memory modules of the latest
generation, and RDB files are protected by a CRC64 checksum. So we started an
investigation in order to discover for bugs in the
After weeks of work, I (Salvatore) analytically discovered a bug that is not related to the user crash. Oran Agra and Yuval Inbar, that are also contributors of this specification, joined the effort of auditing the code. Salvatore also wrote several fuzz testers modeling the layout of the user data. Even if the fuzzing techniques used could easily find the very complex to replicate bug that was found analytically, no crash was ever seen using the Hash data type, the one used during the crash reported by the user.
Even if apparently
ziplist.c does not contain bugs, or at least we cannot find
them, nor there are often reports of crashes related to a potential bug in this
part of the code, during the review all the programmers involved agreed that
the ziplist code was so complex and had such non trivial side effects that it
was a wise idea to switch to something else. The reason why it was hard to
audit was that a ziplist has the following layout:
<header> <entry> <entry> ... <entry> <end-of-ziplist>
However it is important for Redis that a ziplist can be accessed backward, from
the latest element to the first, in order to model commands such as
in a way to avoid scanning the whole ziplist to just fetch a few elements from
the tail. So each entry, was actually composed of the following two parts:
The previous entry length could change in size from 1 to 5 bytes, in order to use little space to encode small previous entries lengths. However inserting or deleting elements in the middle, while using this particular encoding, may have a cascading effect, where the previous length and even the number of bytes the previous length is encoded, may change and may cascade to the next elements. This was the main source of complexity of ziplist. However other alternatives implementations exist that can prevent this problem and only use local information for each entry.
Listpack takes the good ideas of ziplists and reimplement it in order to create a more compact, faster to parse implementation, that maps directly to simple to audit and understand code. Moreover the single entries representation in listpack were redesigned in order to better exploit the kind of data Redis users normally store in lists and hashes. This document describes the new format.
A listpack is encoded into a single linear chunk of memory. It has a fixed length header of six bytes (instead of ten bytes of ziplist, since we no longer need a pointer to the start of the last element). The header is followed by the listpack elements. In theory the data structure does not need any terminator, however for certain concerns, a special entry marking the end of the listpack is provided, in the form of a single byte with value FF (255). The main advantages of the terminator are the ability to scan the listpack without holding (and comparing at each iteration) the address of the end of the listpack, and to recognize easily if a listpack is well formed or truncated. These advantages are, in the idea of the writer, worth the additional byte needed in the representation.
<tot-bytes> <num-elements> <element-1> ... <element-N> <listpack-end-byte>
The six byte header, composed of the tot-bytes and num-elements fields is encoded in the following way:
tot-bytes: 32 bit unsigned integer holding the total amount of bytes representing the listpack. Including the header itself and the terminator. This basically is the total size of the allocation needed to hold the listpack and allows to jump at the end in order to scan the listpack in reverse order, from the last to the first element, when needed.
num-elements: 16 bit unsigned integer holding the total number of elements the listpack holds. However if this field is set to 65535, which is the greatest unsigned integer representable in 16 bit, it means that the number of listpack elements is not known, so a LIST-LENGTH operation will require to fully scan the listpack. This happens when, at some point, the listpack has a number of elements equal or greater than 65535. The num-elements field will be set again to a lower number the first time a LIST-LENGTH operation detects the elements count returned in the representable range.
All integers in the listpack are stored in little endian format, if not otherwise specified (certain special encodings are in big endian because it is more natural to represent them in this way for the way the specification maps to C code).
Each element in a listpack has the following structure:
<encoding-type><element-data><element-tot-len> | | +--------------------------------------------+ (This is an element)
The element type and element total length are always present. The element data itself sometimes is missing, since certain small elements are directly represented inside the spare bits of the encoding type.
The encoding type is basically useful to understand what kind of data follows since strings can be encoded as little endian integers, and strings can have multiple string length fields bits in order to reduce space usage. The element data is the data itself, like an integer or an array of bytes representing a string. Finally the element total length, is used in order to traverse the list backward from the end of the listpack to its head, and is needed since otherwise there is no unique way to parse the entry from right to left, so we need to be able to jump to the left of the specified amount of bytes.
Each element can always be parsed left-to-right. The first two bits of the first byte select the encoding. There are a total of 3 possibilities. The first two encodings represents small strings. The third encoding instead is used in order to specify other sub-encodings.
Strings that can be represented as small numbers, such as "65" or "1" are a very common, so they have a special encoding that allows to specify such strings representing numbers from 0 to 127 as a single byte:
xxxxxxx is a 7 bit unsigned integer. We can test for this encoding
just checking that the most significant bit of the first byte of the
entry is zero.
A few examples:
"\x03" -- The string "3" "\x12" -- The string "18"
Small strings are also very common elements inside objects represented inside Redis collections, so the overhead to specify their length is just a single byte:
This encoding represents strings up to 63 characters in length, since
is a 6 bit unsigned integer. The string data is the byte by byte string itself,
and may be missing in the special case of the empty string.
A few examples:
"\x40" -- The empty string "\x45hello" -- The string "hello"
Multi byte encodings
If the most significant two bits of the first byte are both set, then the remaining bits select one of the following sub encodings.
The first three sub encodings happen when the first two bits are both "11" but the following bits are never "11".
110|xxxxx yyyyyyyy -- 13 bit signed integer 1110|xxxx yyyyyyyy -- string with length up to 4095
In this encoding,
xxxx|yyyyyyyy represent an unsigned integer where
are the most significant bits and
yyyyyyyy are the least significant bits.
Finally, when the first four bits are all set, the following sub encodings represented by the remaining four bits are defined:
1111|0000 <4 bytes len> <large string> 1111|0001 <16 bits signed integer> 1111|0010 <24 bits signed integer> 1111|0011 <32 bits signed integer> 1111|0100 <64 bits signed integer> 1111|0101 to 1111|1110 are currently not used. 1111|1111 End of listpack
Element total length field
As already specified, the last part of an entry is a representation of its own size, so that the listpack can be traversed from right to left. This field has a variable length, so that we use just a single byte for it if the length of the field is small, and progressively use more bytes for bigger entries. The total length field is designed to be parsed from right to left, since this is how we use it, and cannot be parsed the other way around, from left to right. However, when we parse the entry from left to right, we already know its length at the time we need to parse the total length field, so we can also compute how much bytes are needed in order to represent its total length field using the variable encoding. This allows to just skip this amount of bytes without attempting to parse it. We'll make it more clear with examples later in this section.
The variable length is stored from right to left, and the most significant bit of each byte is used in order to signal if there are more bytes. This means that we use only 7 bits in every byte. A entry length smaller than 128 can just be encoded as an 8-bit unsigned integer having the entry value.
"\x20" -- 32 bytes entry length
However if I want to encode a entry length with the value of, for example, 500, two bytes will be required. The binary representation of 500 is the following:
We can split the representation in two 7-bit halves:
Note that, since we parse the entry length from right to left, the entry is stored in big endian (but it's not vanilla big endian since only 7 bits are used and the 8th bit is used to signal the more bytes condition).
However we need to also add the bit to signal if there are more bytes, so the final representation will be:
0000011 1110100 | | `- no more bytes `- more bytes to the left!
The actual encoding will be:
"\xf4\x03" -- 500 bytes entry length
Let's take for example a very simple entry encoding the string "hello":
"\x45hello" -- The string "hello"
The raw entry is 6 bytes: the encoding byte followed by the raw data. In order for the entry to be complete, we need to add the entry length field at the end, that in this case is just the byte "06". So the final complete entry will be:
"\x45hello\x06" -- A complete entry representing "hello"
Note that we can easily parse the entry from right to left, by reading the length of 6, and jumping 6 bytes on the left to reach the start of the entry, but we can also parse the entry from left to right, since after we parsed the entry data of six bytes, we know how much bytes are used in order to encode its length by using the following table:
From 0 to 127: 1 byte From 128 to 16383): 2 bytes From 16383 to 2097151: 3 bytes From 2097151 to 268435455: 4 bytes From 268435455 to 34359738367: 5 bytes
No entry can be longer than 34359738367 bytes.
The wish list about the implementation is, with points in decreasing order of importance, the following:
- Crash resistant against wrong encodings. This was not the case with ziplist implementation.
- Understandable and easily auditable. Well commented code.
- Fast. Avoid unnecessary copying. For instance, when adding to head, detect if realloc() is a non-OP (when advanced malloc functionalities are available) and instead use malloc() and avoid a copy of the data by copying directly at the right offset.
- Availability of an update-element operation, so that if an element is updated with one of the same size (very common with Hashes, think HINCRBY) there is no memory copying involved.
Notes about understandability:
Note that understandability cannot be obtained without simplicity of the design, however the design outlined in this document is thought to have a straightforward translation to a simple and robust implementation.
Notes about crash resistance:
It is worth noting that crash resistance has limitations: for example a corrupted listpack header may make the program jump to invalid addresses. In this context for crash resistance we mean that as long as the corruption does not force the program to jump to illegal addresses, wrong encodings are detected when possible (that is, when the corruption does not happen to map to valid entries). For instance a wrong string length will be detected every time the amount of remaining bytes in the listpack is not compatible with the announced string length. The API should always be able to report such errors instead of crashing the program.
This specification was written by Salvatore Sanfilippo. Oran Agra and Yuval Inbar, together with the author of this spec analyzed the ziplist implementation in order to search for bugs and to understand how the specification could be improved.
Yuval provided the idea of allowing backward traversal by using only information which is local to the entry (the entry length at the end of the entry itself) instead of global informations (such as the length of the previous entry, as it was in ziplist).
Yuval also suggested to use a progressive length integer for the back length.
Oran provided ideas about the optimization of the implementation.
APPENDIX A: potential optimizations not exploited
There are certain improvements that we left out of this specification in order to enhance the simplicity of this data structure.
Different encodings for positive and negative integers.
In theory it is possible to better exploit the fact we have free additional encoding type bits, in order to distinguish between positive and negative integers and always represent them as unsigned. In this way we could improve the range of the integers we can represent with a given number of bytes. A former version of this specification used an encoding like the following:
1111|0001 <16 bits unsigned integer> 1111|0010 <16 bits negative integer> 1111|0011 <24 bits unsigned integer> 1111|0100 <24 bits negative integer> 1111|0101 <32 bits unsigned integer> 1111|0110 <32 bits negative integer> 1111|0111 <64 bits unsigned integer> 1111|1000 <64 bits negative integer>
However at a second thought this was believed to make the implementation more complex and potentially slower, so the slightly less efficient representation of storing signed integers was chosen instead.
Many element in a listpack, notably hash field names representing objects inside
Redis, are going to use a subset of characters in the range
are strings such as
suername and so forth.
Using six bits per character it is possible to represent the alphabet consisting
of all the lower and upper case letters, the numbers from 0 to 9, and a few more
.. So an additional encoding representing strings using
six bits per character could be added in order to improve the space efficiency
of strings considerably.
This was not added mainly for performance considerations, since the complexity added is believed to be manageable and not a likely source of potential bugs.
Accessing far elements in a long listpack is O(N), so it looks natural to add some way in order to speedup this kind of lookups with skip tables. While this is usually a great idea for rarely changing packed representations of data, listpacks are going to be used in situations where data is often changed in the middle (Redis Hash and List data types both stress this usage pattern).
Updating the skip indexes could be error prone and even costly, and with the default settings Redis only uses relatively small listpacks where the access locality well compensates the need for scanning.
When a memory saving representation is needed, with the ability to scale to many elements, the author believes that a linked data structure where listpacks are used as nodes is the preferred approach: it improves separation of concerns between the two representations and may be simpler to manage. In this regard listpacks are very friendly because they can be split and merged easily with linear copies without offsets adjustments.