couchdb-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Apache Wiki <>
Subject [Couchdb Wiki] Update of "TechnicalOverview" by NoahSlater
Date Sat, 29 Mar 2008 23:46:13 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Couchdb Wiki" for change notification.

The following page has been changed by NoahSlater:

The comment on the change is:
Page hosted on main CouchDB website

+ deleted
- ## page was renamed from Technical Overview
- ## page was renamed from TechnicalOverview
- A technical overview of the CouchDB document-oriented database system.
- This overview is intended to give a high-level introduction of key models and components
of CouchDB, how they work individually and how they fit together.
- == Storage ==
- A CouchDB server hosts named databases, which store documents. Each document is uniquely
named in the database, and CouchDB provides a [ RESTful]
HTTP API for reading and updating (add, edit, delete) database documents.
- Documents are the primary unit of data in CouchDB and consist of any number of fields and
binary blobs. Documents also include metadata that’s maintained by the database system.
- Document fields are uniquely named and contain an ordered list of elements. Elements can
be of varying types (text, number, date, time), and there is no set limit to text size or
element count. Binary blobs also are uniquely named.
- The CouchDB document update model is lockless and optimistic. Document edits are made by
client applications loading documents, applying changes, and saving them back to the database.
If another client editing the same document saves their changes first, the client gets an
edit conflict error on save. To resolve the update conflict, the latest document version can
be opened, the edits reapplied and the update tried again.
- Document updates (add, edit, delete) are all or nothing, either succeeding entirely or failing
completely. The database never contains partially saved or edited documents.
- === ACID Properties ===
- The CouchDB file layout and commitment system features all Atomic Consistent Isolated Durable
(ACID) properties. On-disk, CouchDB never overwrites committed data or associated structures,
ensuring the database file is always in a consistent state. This is a “crash-only" design
where the CouchDB server does not go through a shut down process, it's simply terminated.
- Document updates (add, edit, delete) are serialized, except for binary blobs which are written
concurrently. Database readers are never locked out and never have to wait on writers or other
readers. Any number of clients can be reading documents without being locked out or interrupted
by concurrent updates, even on the same document. CouchDB read operations use a Multi-Version
Concurrency Control ([ MVCC])
model where each client sees a consistent snapshot of the database from the beginning to the
end of the read operation.
- Documents are indexed in b-trees by their name (DocID) and a Sequence ID. Each update to
a database instance generates a new sequential number. Sequence IDs are used later for incrementally
finding changes in a database. Theses b-tree indexes are updated simultaneously when documents
are saved or deleted. The index updates always occur at the end of the file (append-only updates).
- Documents have the advantage of data being already conveniently packaged for storage rather
than split out across numerous tables and rows in most databases systems. When documents are
committed to disk, the document fields and metadata are packed into buffers, sequentially
one document after another (helpful later for efficient building of views).
- When CouchDB documents are updated, all data and associated indexes are flushed to disk
and the transactional commit always leaves the database in a completely consistent state.
Commits occur in two steps: 1. All document data and associated index updates are synchronously
flushed to disk. 2. The updated database header is written in two consecutive, identical chunks
to make up the first 4k of the file, and then synchronously flushed to disk.
- In the event of an OS crash or power failure during step 1, the partially flushed updates
are simply forgotten on restart. If such a crash happens during step 2 (committing the header),
a surviving copy of the previous identical headers will remain, ensuring coherency of all
previously committed data. Excepting the header area, consistency checks or fix-ups after
a crash or a power failure are never necessary.
- === Compaction ===
- Wasted space is recovered by occasional compaction. On schedule, or when the database file
exceeds a certain amount of wasted space, the compaction process clones all the active data
to a new file and then discards the old file. The database remains completely online the entire
time and all updates and reads are allowed to complete successfully. The old file is deleted
only when all the data has been copied and all users transitioned to the new file.
- == Views ==
- ACID properties only deal with storage and updates, we also need the ability to show our
data in interesting and useful ways. Unlike SQL databases where data must be carefully decomposed
into tables, data in CouchDB is stored in semi-structured documents. CouchDB documents are
flexible and each has its own implicit structure, which alleviates the most difficult problems
and pitfalls of bi-directionally replicating table schemas and their contained data.
- But beyond acting as a fancy file server, a simple document model for data storage and sharing
is too simple to build real applications on - it simply doesn't do enough of the things we
want and expect. We want to slice and dice and see our data in many different ways. What is
needed is a way to filter, organize and report on data that hasn't already been decomposed
into tables.
- === View model ===
- To address this problem of adding structure back to unstructured and semi-structured data,
CouchDB integrates a view model. Views are the method of aggregating and reporting on the
documents in a database, and are built on-demand to aggregate, join and report on database
documents. Views are built dynamically and don’t affect the underlying document, you can
have as many different view representations of the same data as you like.
- View definitions are strictly virtual and only display the documents from the current database
instance, making them separate from the data they display and compatible with replication.
CouchDB views are defined inside special "design" documents and can replicate across database
instances like regular documents, so that not only data replicates in CouchDB, but entire
application designs replicate too.
- === Javascript View Functions ===
- Views are defined using Javascript functions acting as the map part in a [
map-reduce system]. A view function takes a CouchDB document as an argument and then does
whatever computation it needs to do to determine if it should be in the view or not. The function
simply returns the document or a subset of its data if it should be in the View or nothing
if not.
- === View Indexes ===
- Views are a dynamic representation of the actual document contents of a database, and CouchDB
makes it easy to create useful views of data. But generating a view of a database with hundreds
of thousands or millions of documents is time and resource consuming, it's not something the
system should do from scratch each time.
- To keep view querying fast, the view engine maintains cached indexes of its views, and incrementally
updates them to reflect changes in the database. CouchDB’s core design is largely optimized
around the need for efficient, incremental creation of views and their indexes.
- Views and their functions are defined inside special “design” documents, and a design
document may contain any number of uniquely named view formulas. When a user opens a view
and its index is automatically updated, all the views in the same design document are indexed
as a single group.
- The view builder uses the database Sequence ID to determine if the view group is fully up-to-date
with the database. If not, the view engine examines the all database documents (in packed
sequential order) changed since the last refresh. Documents are read in the order they occur
in the disk file, reducing the frequency and cost of disk head seeks.
- The views can be read and queried simultaneously while being also being refreshed. If a
client is slowly streaming out the contents of a large view, the same view can be concurrently
opened and refreshed for another client without blocking the first client. This is true for
any number of simultaneous client readers, who can read and query the view while the index
is concurrently being refreshed for other clients without causing problems for the readers.
- As documents are examined, their previous row values are removed from the view indexes,
if they exist. If the document is selected by a view function, the function results are inserted
into the view as a new row.
- When view index changes are written to disk, the updates are always appended at the end
of the file, serving to both reduce disk head seek times during disk commits and to ensure
crashes and power failures can not cause corruption of indexes. If a crash occurs while updating
a view index, the incomplete index updates are simply lost and rebuilt incrementally from
its previously committed state.
- == Security and Validation ==
- To protect who can read and update documents, CouchDB has a simple reader access and update
validation model that can be extended to implement custom security models.
- === Administrator Access ===
- CouchDB database instances have administrator accounts. Administrator accounts can create
other administrator accounts and update design documents. Design documents are special documents
containing view definitions and other special formulas, as well as regular fields and blobs.
- === Reader Access ===
- To protect document contents, CouchDB documents can have a reader list. This is an optional
list of reader-names allowed to read the document. When a reader list is used, protected documents
are only viewable by listed users.
- When a user accesses a database, the his/her credentials (name and password) are used to
dynamically determine his reader names. The user credentials are input to a javascript function
and the function returns a list of names for the user, or an error if the user credentials
are wrong.
- When a document is protected by reader access lists, any user attempting to read the document
must be listed. Reader lists are enforced in views too. Documents that are not allowed to
be read by the user are dynamically filtered out of views, keeping the document row and extracted
information invisible to non-readers.
- === Update Validation ===
- As documents written to disk, they can be validated dynamically by javascript functions
for both security and data validation. When the document passes all the formula validation
criteria, the update is allowed to continue. If the validation fails, the update is aborted
and the user client gets an error response.
- Both the user's credentials and the updated document are given as inputs to the validation
formula, and can be used to implement custom security models by validating a user's permissions
to update a document.
- A basic “author only” update document model is trivial to implement, where document
updates are validated to check if the user is listed in an “author” field in the existing
document. More dynamic models are also possible, like checking a separate user account profile
for permission settings.
- The update validations are enforced for both live usage and replicated updates, ensuring
security and data validation in a shared, distributed system.
- == Distributed Updates and Replication ==
- CouchDB is a peer-based distributed database system, it allows for users and servers to
access and update the same shared data while disconnected and then bi-directionally replicate
those changes later.
- The CouchDB document storage, view and security models are designed to work together to
make true bi-directional replication efficient and reliable. Both documents and designs can
replicate, allowing full database applications (including application design, logic and data)
to be replicated to laptops for offline use, or replicated to servers in remote offices where
slow or unreliable connections make sharing data difficult.
- The replication process is incremental. At the database level, replication only examines
documents updated since the last replication. Then for each updated document, only fields
and blobs that have changed are replicated across the network. If replication fails at any
step, due to network problems or crash for example, the next replication restarts at the same
document where it left off.
- Partial replicas can be created and maintained. Replication can be filtered by a javascript
function, so that only particular documents or those meeting specific criteria are replicated.
This can allow users to take subsets of a large shared database application offline for their
own use, while maintaining normal interaction with the application and that subset of data.
- === Conflicts ===
- Conflict detection and management are key issues for any distributed edit system. The CouchDB
storage system treats edit conflicts as a common state, not an exceptional one. The conflict
handling model is simple and "non-destructive" while preserving single document semantics
and allowing for decentralized conflict resolution.
- CouchDB allows for any number of conflicting documents to exist simultaneously in the database,
with each database instance deterministically deciding which document is the “winner”
and which are conflicts. Only the winning document can appear in views, while “losing”
conflicts are still accessible and remain in the database until deleted or purged during database
compaction. Because conflict documents are still regular documents, they replicate just like
regular documents and are subject to the same security and validation rules.
- When distributed edit conflicts occur, every database replica sees the same winning revision
and each has the opportunity to resolve the conflict. Resolving conflicts can be done manually
or, depending on the nature of the data and the conflict, by automated agents. The system
makes decentralized conflict resolution possible while maintaining single document database
- The following view function indexes all documents with conflicts, which can be helpful for
automated resolution:
- {{{
- function(doc) { if(doc.conflicts) { map(null, doc); }}
- }}}
- Conflict management continues to work even if multiple disconnected users or agents attempt
to resolve the same conflicts. If resolved conflicts result in more conflicts, the system
accommodates them in the same manner, determining the same winner on each machine and maintaining
single document semantics.
- === Applications ===
- Using just the basic replication model, many traditionally single server database applications
can be made distributed with almost no extra work. CouchDB replication is designed to be immediately
useful for basic database applications, while also being extendable for more elaborate and
full-featured uses.
- With very little database work, it is possible to build a distributed document management
application with granular security and full revision histories. Updates to documents can be
implemented to exploit incremental field and blob replication, where replicated updates are
nearly as efficient and incremental as the actual edit differences ("diffs").
- The CouchDB replication model can be modified for other distributed update models. If the
storage engine is enhanced to allow multi-document update transactions, it is possible to
perform Subversion-like “all or nothing” atomic commits when replicating with an upstream
server, such that any single document conflict or validation failure will cause the entire
update to fail. Like Subversion, conflicts would be resolved by doing a “pull” replication
to force the conflicts locally, then merging and re-replicating to the upstream server.
- == Implementation ==
- CouchDB is built on the [ Erlang OTP platform], a functional, concurrent
programming language and development platform. Erlang was developed for real-time telecom
applications with an extreme emphasis on reliability and availability.
- Both in syntax and semantics, Erlang is very different from conventional programming languages
like C or Java. Erlang uses lightweight "processes" and message passing for concurrency, it
has no shared state threading and all data is immutable. The robust, concurrent nature of
Erlang is ideal for a database server.
- CouchDB is designed for lock-free concurrency, in the conceptual model and the actual Erlang
implementation. Reducing bottlenecks and avoiding locks keeps the entire system working predictably
under heavy loads. CouchDB can accommodate many clients replicating changes, opening and updating
documents, and querying views whose indexes are simultaneously being refreshed for other clients,
without needing locks.
- For higher availability and more concurrent users, CouchDB is designed for "shared nothing"
clustering. In a "shared nothing" cluster, each machine is independent and replicates data
with its cluster mates, allowing individual server failures with zero downtime. And because
consistency scans and fix-ups aren’t needed on restart, if the entire cluster fails –
due to a power outage in a datacenter, for example – the entire CouchDB distributed system
becomes immediately available after a restart.
- CouchDB is built from the start with a consistent vision of a distributed document database
system. Unlike cumbersome attempts to bolt distributed features on top of the same legacy
models and databases, it is the result of careful ground-up design, engineering and integration.
The document, view, security and replication models, the special purpose query language, the
efficient and robust disk layout and the concurrent and reliable nature of the Erlang platform
are all carefully integrated for a reliable and efficient system.

View raw message