ADL is short for Advanced Data Layout. See docs for Advanced Data Layouts.
In brief: ADLs a concept in IPLD which describes when pluggable code is used to create data structures which act like typical Data Model Nodes (meaning regular generic code can work over them just like over plain Nodes), while having internal structure which is managed by the plugin code.
ADLs are typically used to do things like create large sharded data structures which can be accessed in the same way as simpler structures, but the definition is fairly open-ended.
The term "block" refers to to a chunk of serialized binary data.
Most users don't work directly with blocks. Instead, block binary data is encoded and decoded to and from the IPLD Data Model using a codec, and users work with the data via the Data Model. It's only if writing a storage system or a data transport system that you will be likely to work directly with blocks.
CID stands for Content IDentifier. It's a self-describing data structure identifier. In other words, it's a hash that says what kind of hash it is and how to decode the binary data identified by the hash.
See the CID specification for further details.
CIDs are for Content Addressing.
"Content addressability" refers to the ability to refer to content by a trustless identifier.
Rather than referring to content by an imprecise name or a location-oriented concept like URL, content addressable systems refer to content by a cryptographic hash of the content itself. This allows complete decentralization of the content, as the identifier does not specify the retrieval method nor locations, and does provides a secure way to verify the content (regardless of wherever it may be found from).
DAG is short for Directed Acyclic Graph. It describes some data where more than one path can lead to the same point, but there are only a finite number of paths, because the data does not include cycles.
Read more about DAGs on Wikipedia.
The IPLD Data Model describes common base types that we call kinds. ("types" is a term that we prefer to reserve for data structures described by IPLD Schemas.)
These kinds allow IPLD to create data structures using simple types accessible across many programming languages and encoding formats.
Using the Data Model we can implement file systems, databases, and custom application data structures in a format agnostic way and even link between these structures and formats using common toolchains.
The Data Model kinds are:
(You may notice the Data Model kinds are essentially what you're familiar with from JSON --
we've just added
bytes for binary, and this
link kind, which gives IPLD a lot of its magic.)
There is a
link kind is implemented by CIDs.
DMT is short for "Data Model Tree". It is a term we coined in IPLD to describe data that's -- well -- in the Data Model form.
The term "DMT" is usually only used when necessary for differentiation: for example, we might say: "this data isn't in JSON form anymore; we've parsed it into DMT form". Another example sentence might be: "this data was created with a library DSL, but really, its true form is a standard DMT". And so on.
The concept of DMT could also be compared to the computer science concept of an AST, but again, sometimes a unique term is useful for disambiguation. For example: "the DSL AST is somewhat richer than the DMT; the DMT only describes the logical elements of the document rather than the whole syntax used to specify them". (We also find the term "AST" somewhat of a bad match for what we mean by DMTs in IPLD, because an IPLD DMT is explicitly codec agnostic (in other words, syntax agnostic!), which doesn't line up well with the "S" in "AST".)
In IPLD, we use the word "kind" to refer to one of the handful of basic sorts of data we can recognize and know how to operate on. For example, "string" and "boolean" and "map" are all examples of kinds.
A link is just another name for a CID. When we talk about linking in IPLD, we always mean this -- linking in IPLD is always immutable, and uses hashes, and therefore when we talk about linking, we always mean CIDs.
A "node" in IPLD is a point in a graph -- an element of the Data Model in an instantiated data structure. Every node has a "kind" property, which is one of the Data Model kinds.
If a node is a
list kind, then it will have children.
The other node kinds, like
string, are just values (they have no children).
A "block" will typically contain many nodes.
IPLD Schemas are a system for describing data with structural types. That means Schemas can describe data, and the data either matches, or, doesn't -- and if it doesn't, you can just try a different schema.
Schemas are a high-level feature in IPLD. You can apply them on top of data that's already legible as Data Model content. That means IPLD Schemas are agnostic of codecs. It also means they're entirely optional -- you can parse data with or without them -- and you can use Schemas to describe and help process data even if that data predates the Schema.
IPLD Selectors are a form of graph query (or, a way to specify a traversal, if you prefer that mental model) over IPLD data.
Selectors are a declarative format for specifying a walk over a Data Model graph. Selectors can specify which nodes to walk over (or not), which order to visit in, and it can mark certain positions as "matched" (in addition to just visited). You can think of Selectors as being roughly like "regexps for graphs". Libraries may yield iterators over matched nodes, or iterators over all visited nodes, or callback oriented interfaces.
Selectors are natively implemented in most IPLD libraries (for performance reasons), but the format itself is standardized. The format is described in IPLD (using IPLD Schemas, so it's possible to serialize Selectors in any Codec you want, and it's also possible to inspect (and transform!) Selector documents using standard Data Model tools.
"Substrate" is a vocabulary term relating to ADLs -- it refers to the data "inside" them, as contrasted with the "synthesized view" of the data, which is the node that the ADL presents itself as. (The substrate may also sometimes colloquially be called "content data" or "encoded form" or other terms.)
Traversal is the act of walking across the Data Model.
It is useful to consider the Data Model as being formed of "scalar" and "recursive" kinds when considering nodes and possible traversals.
"Scalar" kinds are terminal nodes in the Data Model: null, boolean, integer, float, string, bytes
"Recursive" kinds can contain other kinds within them and therefore allow deeper traversal: map and list.
The link kind is scalar, but is typically treated as a transparent node for the purpose of traversal such that data spanning many blocks can be addressed as a single graph of nodes (so, sometimes, contextually, it can be seen as a sort of a "recursive" kind).