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new b9e323bd54 MINOR: Fix typo in design.md (#836)
b9e323bd54 is described below
commit b9e323bd54ee931d14ce1b0e1c26cc8f4b283a01
Author: Alexander Pruss <[email protected]>
AuthorDate: Fri Apr 17 13:57:33 2026 +0200
MINOR: Fix typo in design.md (#836)
Reviewers: Mickael Maison <[email protected]>
---
content/en/42/design/design.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/content/en/42/design/design.md b/content/en/42/design/design.md
index 01c83590a5..fa99468ff2 100644
--- a/content/en/42/design/design.md
+++ b/content/en/42/design/design.md
@@ -386,7 +386,7 @@ Log compaction gives us a more granular retention mechanism
so that we are guara
Let's start by looking at a few use cases where this is useful, then we'll see
how it can be used.
- 1. _Database change subscription_. It is often necessary to have a data set
in multiple data systems, and often one of these systems is a database of some
kind (either a RDBMS or perhaps a new-fangled key-value store). For example you
might have a database, a cache, a search cluster, and a Hadoop cluster. Each
change to the database will need to be reflected in the cache, the search
cluster, and eventually in Hadoop. In the case that one is only handling the
real-time updates you only [...]
+ 1. _Database change subscription_. It is often necessary to have a data set
in multiple data systems, and often one of these systems is a database of some
kind (either a RDBMS or perhaps a new-fangled key-value store). For example you
might have a database, a cache, a search cluster, and a Hadoop cluster. Each
change to the database will need to be reflected in the cache, the search
cluster, and eventually in Hadoop. In the case that one is only handling the
real-time updates you only [...]
2. _Event sourcing_. This is a style of application design which co-locates
query processing with application design and uses a log of changes as the
primary store for the application.
3. _Journaling for high-availability_. A process that does local computation
can be made fault-tolerant by logging out changes that it makes to its local
state so another process can reload these changes and carry on if it should
fail. A concrete example of this is handling counts, aggregations, and other
"group by"-like processing in a stream query system. Samza, a real-time
stream-processing framework, [uses this
feature](https://samza.apache.org/learn/documentation/0.7.0/container/s [...]
In each of these cases one needs primarily to handle the real-time feed of
changes, but occasionally, when a machine crashes or data needs to be re-loaded
or re-processed, one needs to do a full load. Log compaction allows feeding
both of these use cases off the same backing topic. This style of usage of a
log is described in more detail in [this blog
post](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying).
