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nexedi
ZODB
Commits
551f6a6c
Commit
551f6a6c
authored
Mar 14, 2020
by
Éloi Rivard
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doc/conf.py
doc/conf.py
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doc/index.rst
doc/index.rst
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doc/introduction.rst
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doc/conf.py
View file @
551f6a6c
...
...
@@ -210,4 +210,5 @@ intersphinx_mapping = {
"persistent"
:
(
'https://persistent.readthedocs.io/en/latest/'
,
None
),
"zodburi"
:
(
"https://docs.pylonsproject.org/projects/zodburi/en/latest/"
,
None
),
"btrees"
:
(
"https://btrees.readthedocs.io/en/latest/"
,
None
),
"zodburi"
:
(
"https://docs.pylonsproject.org/projects/zodburi/en/latest/"
,
None
),
}
doc/index.rst
View file @
551f6a6c
...
...
@@ -21,188 +21,13 @@ Check out the :doc:`tutorial`!
ZODB runs on Python 2.7 or Python 3.4 and above. It also runs on PyPy.
Transactions
============
Transactions make programs easier to reason about.
Transactions are atomic
Changes made in a transaction are either saved in their entirety or
not at all.
This makes error handling a lot easier. If you have an error, you
just abort the current transaction. You don't have to worry about
undoing previous database changes.
Transactions provide isolation
Transactions allow multiple logical threads (threads or processes)
to access databases and the database prevents the threads from
making conflicting changes.
This allows you to scale your application across multiple threads,
processes or machines without having to use low-level locking
primitives.
You still have to deal with concurrency on some level. For
timestamp-based systems like ZODB, you may have to retry conflicting
transactions. With locking-based systems, you have to deal with
possible deadlocks.
Transactions affect multiple objects
Most NoSQL databases don't have transactions. Their notions of
consistency are much weaker, typically applying to single documents.
There can be good reasons to use NoSQL databases for their extreme
scalability, but otherwise, think hard about giving up the benefits
of transactions.
ZODB transaction support:
- `ACID <https://en.wikipedia.org/wiki/ACID>`_ transactions with
`snapshot isolation
<https://en.wikipedia.org/wiki/Snapshot_isolation>`_
- Distributed transaction support using two-phase commit
This allows transactions to span multiple ZODB databases and to span
ZODB and non-ZODB databases.
Other notable ZODB features
===========================
Database caching with invalidation
Every database connection has a cache that is a consistent partial database
replica. When accessing database objects, data already in the cache
is accessed without any database interactions. When data are
modified, invalidations are sent to clients causing cached objects
to be invalidated. The next time invalidated objects are accessed
they'll be loaded from the database.
Applications don't have to invalidate cache entries. The database
invalidates cache entries automatically.
Pluggable layered storage
ZODB has a pluggable storage architecture. This allows a variety of
storage schemes including memory-based, file-based and distributed
(client-server) storage. Through storage layering, storage
components provide compression, encryption, replication and more.
Easy testing
Because application code rarely has database logic, it can
usually be unit tested without a database.
ZODB provides in-memory storage implementations as well as
copy-on-write layered "demo storage" implementations that make testing
database-related code very easy.
Garbage collection
Removal of unused objects is automatic, so application developers
don't have to worry about referential integrity.
Binary large objects, Blobs
ZODB blobs are database-managed files. This can be especially
useful when serving media. If you use AWS, there's a Blob
implementation that stores blobs in S3 and caches them on disk.
Time travel
ZODB storages typically add new records on write and remove old
records on "pack" operations. This allows limited time travel, back
to the last pack time. This can be very useful for forensic
analysis.
When should you use ZODB?
=========================
You want to focus on your application without writing a lot of database code.
ZODB provides highly transparent persistence.
Your application has complex relationships and data structures.
In relational databases you have to join tables to model complex
data structures and these joins can be tedious and expensive. You
can mitigate this to some extent in databases like Postgres by using
more powerful data types like arrays and JSON columns, but when
relationships extend across rows, you still have to do joins.
In NoSQL databases, you can model complex data structures with
documents, but if you have relationships across documents, then you
have to do joins and join capabilities in NoSQL databases are
typically far less powerful and transactional semantics typically don't
cross documents, if they exist at all.
In ZODB, you can make objects as complex as you want and cross
object relationships are handled with Python object references.
You access data through object attributes and methods.
If your primary object access is search, then other database
technologies might be a better fit.
ZODB has no query language other than Python. It's primary support
for search is through mapping objects called BTrees. People have
build higher-level search APIs on top of ZODB. These work well
enough to support some search.
You read data a lot more than you write it.
ZODB caches aggressively, and if your working set fits (or mostly
fits) in memory, performance is very good because it rarely has to
touch the database server.
If your application is very write heavy (e.g. logging), then you're
better off using something else. Sometimes, you can use a database
suitable for heavy writes in combination with ZODB.
Need to test logic that uses your database.
ZODB has a number of storage implementations, including layered
in-memory implementations that make testing very easy.
A database without an in-memory storage option can make testing very
complicated.
When should you *not* use ZODB?
===============================
- You have very high write volume.
ZODB can commit thousands of transactions per second with suitable
storage configuration and without conflicting changes.
Internal search indexes can lead to lots of conflicts, and can
therefore limit write capacity. If you need high write volume and
search beyond mapping access, consider using external indexes.
- You need to use non-Python tools to access your database.
especially tools designed to work with relational databases
Newt DB addresses these issues to a significant degree. See
http://newtdb.org.
How does ZODB scale?
====================
Not as well as many technologies, but some fairly large applications
have been built on ZODB.
At Zope Corporation, several hundred newspaper content-management
systems and web sites were hosted using a multi-database configuration
with most data in a main database and a catalog database. The
databases had several hundred gigabytes of ordinary database records
plus multiple terabytes of blob data.
ZODB is mature
==============
ZODB is very mature. Development started in 1996 and it has been used
in production in thousands of applications for many years.
ZODB is in heavy use in the `Pyramid <http://www.pylonsproject.org/>`_
and `Plone <https://plone.org/>`_ communities and in many other
applications.
Learning more
=============
.. toctree::
:maxdepth: 1
introduction
tutorial
guide/index
reference/index
...
...
doc/introduction.rst
0 → 100644
View file @
551f6a6c
============
Introduction
============
Transactions
============
Transactions make programs easier to reason about.
Transactions are atomic
Changes made in a transaction are either saved in their entirety or
not at all.
This makes error handling a lot easier. If you have an error, you
just abort the current transaction. You don't have to worry about
undoing previous database changes.
Transactions provide isolation
Transactions allow multiple logical threads (threads or processes)
to access databases and the database prevents the threads from
making conflicting changes.
This allows you to scale your application across multiple threads,
processes or machines without having to use low-level locking
primitives.
You still have to deal with concurrency on some level. For
timestamp-based systems like ZODB, you may have to retry conflicting
transactions. With locking-based systems, you have to deal with
possible deadlocks.
Transactions affect multiple objects
Most NoSQL databases don't have transactions. Their notions of
consistency are much weaker, typically applying to single documents.
There can be good reasons to use NoSQL databases for their extreme
scalability, but otherwise, think hard about giving up the benefits
of transactions.
ZODB transaction support:
- `ACID <https://en.wikipedia.org/wiki/ACID>`_ transactions with
`snapshot isolation
<https://en.wikipedia.org/wiki/Snapshot_isolation>`_
- Distributed transaction support using two-phase commit
This allows transactions to span multiple ZODB databases and to span
ZODB and non-ZODB databases.
Other notable ZODB features
===========================
Database caching with invalidation
Every database connection has a cache that is a consistent partial database
replica. When accessing database objects, data already in the cache
is accessed without any database interactions. When data are
modified, invalidations are sent to clients causing cached objects
to be invalidated. The next time invalidated objects are accessed
they'll be loaded from the database.
Applications don't have to invalidate cache entries. The database
invalidates cache entries automatically.
Pluggable layered storage
ZODB has a pluggable storage architecture. This allows a variety of
storage schemes including memory-based, file-based and distributed
(client-server) storage. Through storage layering, storage
components provide compression, encryption, replication and more.
Easy testing
Because application code rarely has database logic, it can
usually be unit tested without a database.
ZODB provides in-memory storage implementations as well as
copy-on-write layered "demo storage" implementations that make testing
database-related code very easy.
Garbage collection
Removal of unused objects is automatic, so application developers
don't have to worry about referential integrity.
Binary large objects, Blobs
ZODB blobs are database-managed files. This can be especially
useful when serving media. If you use AWS, there's a Blob
implementation that stores blobs in S3 and caches them on disk.
Time travel
ZODB storages typically add new records on write and remove old
records on "pack" operations. This allows limited time travel, back
to the last pack time. This can be very useful for forensic
analysis.
When should you use ZODB?
=========================
You want to focus on your application without writing a lot of database code.
ZODB provides highly transparent persistence.
Your application has complex relationships and data structures.
In relational databases you have to join tables to model complex
data structures and these joins can be tedious and expensive. You
can mitigate this to some extent in databases like Postgres by using
more powerful data types like arrays and JSON columns, but when
relationships extend across rows, you still have to do joins.
In NoSQL databases, you can model complex data structures with
documents, but if you have relationships across documents, then you
have to do joins and join capabilities in NoSQL databases are
typically far less powerful and transactional semantics typically don't
cross documents, if they exist at all.
In ZODB, you can make objects as complex as you want and cross
object relationships are handled with Python object references.
You access data through object attributes and methods.
If your primary object access is search, then other database
technologies might be a better fit.
ZODB has no query language other than Python. It's primary support
for search is through mapping objects called BTrees. People have
build higher-level search APIs on top of ZODB. These work well
enough to support some search.
You read data a lot more than you write it.
ZODB caches aggressively, and if your working set fits (or mostly
fits) in memory, performance is very good because it rarely has to
touch the database server.
If your application is very write heavy (e.g. logging), then you're
better off using something else. Sometimes, you can use a database
suitable for heavy writes in combination with ZODB.
Need to test logic that uses your database.
ZODB has a number of storage implementations, including layered
in-memory implementations that make testing very easy.
A database without an in-memory storage option can make testing very
complicated.
When should you *not* use ZODB?
===============================
- You have very high write volume.
ZODB can commit thousands of transactions per second with suitable
storage configuration and without conflicting changes.
Internal search indexes can lead to lots of conflicts, and can
therefore limit write capacity. If you need high write volume and
search beyond mapping access, consider using external indexes.
- You need to use non-Python tools to access your database.
especially tools designed to work with relational databases
Newt DB addresses these issues to a significant degree. See
http://newtdb.org.
How does ZODB scale?
====================
Not as well as many technologies, but some fairly large applications
have been built on ZODB.
At Zope Corporation, several hundred newspaper content-management
systems and web sites were hosted using a multi-database configuration
with most data in a main database and a catalog database. The
databases had several hundred gigabytes of ordinary database records
plus multiple terabytes of blob data.
ZODB is mature
==============
ZODB is very mature. Development started in 1996 and it has been used
in production in thousands of applications for many years.
ZODB is in heavy use in the `Pyramid <http://www.pylonsproject.org/>`_
and `Plone <https://plone.org/>`_ communities and in many other
applications.
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