Improve DynamoDB Performance with DAX
Summary
TLDRThis video introduces DynamoDB Accelerator (DAX), a managed caching layer for DynamoDB that offers microsecond-level performance, significantly faster than standard DynamoDB operations. The script covers DAX's use cases, benefits, and limitations, including eventual consistency issues. It also provides an in-depth look at DAX's architecture, explaining how it handles read and write operations, and concludes with best practices for monitoring, scaling, and avoiding common pitfalls when using DAX in applications.
Takeaways
- 🚀 DynamoDB Accelerator (DAX) is a fully managed caching layer for DynamoDB that improves performance by providing microsecond response times.
- 💡 DAX is suitable for applications with consistent or burst traffic on the same set of keys, especially those with limited write patterns.
- 📊 Caching optimizes performance by storing frequently accessed data in memory, reducing the need for repetitive database queries.
- 🔄 DAX supports various operations including get, query, scan, update, delete, and put, as well as LSI and GSI operations.
- 📈 DAX clusters consist of EC2 nodes that can be scaled horizontally (adding more nodes) and vertically (adding more resources to nodes).
- 🖥️ DAX clusters typically have one master node for writes and multiple read replicas to serve read requests.
- ⚠️ Using DAX introduces eventual consistency, meaning there could be a delay in data updates between the cache and the database, potentially serving stale data.
- 🔍 Avoid caching queries if you need consistent data, as the cached query results may not reflect the latest updates.
- 🔧 Monitor and scale your DAX nodes according to your application's workload to ensure optimal performance and cost-efficiency.
- 🔗 Sharing DAX clusters across multiple tables or applications can lead to cascading failures, so it's important to be cautious with this setup.
Q & A
What is DynamoDB Accelerator (DAX)?
-DynamoDB Accelerator, also known as DAX, is a fully managed, highly available caching layer for DynamoDB that delivers microsecond-level performance for read and write operations.
What is the basic function of a cache?
-A cache is a data storage layer that contains a subset of data from the main database. It serves to optimize performance by allowing for quicker data retrieval from in-memory storage rather than from the database itself.
What is the difference between local and remote caches?
-A local cache is stored in the memory of the machine it serves, while a remote cache involves multiple nodes working together to act as a distributed cache, similar to how DAX operates.
How does DAX improve performance compared to standard DynamoDB?
-DAX improves performance by providing microsecond-level response times for read operations, which is significantly faster than the typical milliseconds range for standard DynamoDB get item operations.
What are the scalability options for DAX?
-DAX is both horizontally and vertically scalable. Horizontal scalability is achieved by adding more nodes to the cluster, while vertical scalability involves increasing the CPU, memory, or performance of the existing nodes.
Which operations does DAX support for data retrieval and modification?
-DAX supports get, query, and scan operations for data retrieval, and update, delete, and put operations for data modification. It also supports caching for LSI (Local Secondary Index), GSI (Global Secondary Index), and any range key operations during queries.
What are the use case scenarios where DAX is suitable?
-DAX is suitable for applications with consistent or burst-like traffic on the same set of keys, limited write patterns, and those requiring microsecond-level response times. It is also suitable for read-intensive applications that can tolerate eventual consistency.
What is the concept of eventual consistency in the context of DAX?
-Eventual consistency means that there may be scenarios where the data in the DAX cache is inconsistent with the data in the DynamoDB table, leading to the possibility of serving stale content.
What is the recommended approach when using DAX for applications that require high data accuracy?
-For applications requiring high data accuracy, it is recommended to use the DynamoDB table as the source of truth and avoid relying solely on the DAX cache, especially for critical operations.
What are some best practices when using DAX?
-Best practices include understanding the implications of caching queries, monitoring and scaling nodes based on workload, being aware of single points of failure when sharing a cluster across multiple tables, and treating the DynamoDB table as the source of truth when in doubt.
What is the role of the 'item cache' in DAX?
-The item cache in DAX is responsible for storing mappings of keys to values. It is updated transactionally whenever there are modifications to the items in the DynamoDB table, ensuring that subsequent lookups can be served from the cache.
How does the 'query cache' in DAX work?
-The query cache in DAX works by caching a combination of criteria and a list of results. When a query request comes in, DAX first checks if the criteria have been previously queried. If not, it retrieves the data from DynamoDB and stores it in the cache for faster access in subsequent queries.
What is the potential issue with using the query cache in DAX?
-The potential issue with using the query cache is that it may serve stale data. If any of the items in the cached results change in the DynamoDB table while the cache entry is still valid, the next query using the same criteria may receive outdated results from the cache.
Outlines
🚀 Introduction to DynamoDB Accelerator (DAX)
The video script introduces the concept of DynamoDB Accelerator, known as DAX, which is a highly efficient caching layer for DynamoDB. The speaker expresses excitement about discussing DAX due to their familiarity with it. The video aims to provide an overview of DAX, discuss its suitable use cases, explain its operational mechanism, delve into its inner workings, and conclude with best practice advice. Before diving into DAX, the script mentions a tool called 'Dino bass' designed to simplify working with DynamoDB, offering a free trial and a link in the description for further exploration. The foundation of the discussion is set by explaining the basic concept of caching, its benefits, and how it functions as an in-memory data storage layer to improve performance by reducing database retrieval times.
🛠 Understanding DAX and Its Scalability
This paragraph delves into what DAX is—a fully managed, highly available caching layer for DynamoDB that significantly boosts performance to microsecond levels. It contrasts the typical performance of DynamoDB with the enhanced performance provided by DAX. The script explains that DAX is scalable both horizontally and vertically, with nodes consisting of EC2 machines that work together to serve cached content. It clarifies that one node acts as the master for write operations, while others serve as read replicas. DAX supports common DynamoDB operations like get, query, scan, update, delete, and put, and also caches Local Secondary Index (LSI) and Global Secondary Index (GSI) operations. The transition from standard DynamoDB to DAX is straightforward, typically requiring only a change in the SDK and endpoint. The cost of using DAX is based on a per-node basis, with no additional costs incurred.
🤔 Assessing Suitability for DAX Use
The script discusses the suitability of DAX for different applications, noting that while DAX offers performance benefits, it also has limitations that may make it unsuitable for certain use cases. Ideal candidates for DAX are applications with consistent or bursty traffic on a set of keys that do not change frequently, requiring microsecond-level response times and being able to tolerate eventual consistency. Eventual consistency is explained as a scenario where data in the cache may become inconsistent with the DynamoDB table, leading to potential issues. The paragraph advises against using DAX for write-intensive applications, as caches are more beneficial for read-intensive scenarios with high read and limited write operations.
🔍 Deep Dive into DAX's Internal Mechanisms
The script provides an in-depth look at how DAX operates, starting with a high-level overview of setting up a DAX cluster in front of a DynamoDB table to reduce read capacity costs and improve performance. It explains the process of read and write operations within DAX, including cache misses and the propagation of data from the master node to read replicas. The paragraph then zooms in on the low-level implementation of DAX, detailing the two internal caches: the item cache, which stores key-value mappings and is updated transactionally with table modifications, and the query cache, which stores query results based on criteria. The script highlights the potential issues with query caching, such as serving stale data due to delays in updating the cache in response to table changes.
📘 Best Practices for Using DAX
The final paragraph offers best practice advice for using DAX. It warns against caching queries due to the risk of serving inconsistent data and emphasizes the importance of understanding the implications of using caches for mission-critical information. It also stresses the need for monitoring and scaling DAX nodes based on workload, as well as being cautious about sharing a DAX cluster across multiple tables to prevent potential cascading failures. The advice concludes with a reminder to always consider the DynamoDB table as the source of truth in cases of doubt, to ensure fault tolerance and the best possible user experience.
📺 Conclusion and Future Content Tease
The video script concludes with a prompt for viewers to like and subscribe to stay updated with upcoming content, including a teaser for a future video on DynamoDB schema design. The speaker thanks the audience and signs off, indicating the end of the current video session.
Mindmap
Keywords
💡DynamoDB Accelerator (DAX)
💡Caching
💡In-memory Cache
💡Dino Bass
💡Microsecond-level Performance
💡Horizontal and Vertical Scalability
💡Eventual Consistency
💡Read Replicas
💡Item Cache
💡Query Cache
Highlights
Introduction to DynamoDB Accelerator (DAX) and its ability to significantly improve performance for DynamoDB operations.
DAX, also known as DynamoDB Accelerator, is a fully managed, highly available caching layer for DynamoDB.
DAX delivers microsecond-level performance, a stark contrast to the typical millisecond response times of DynamoDB.
DAX's scalability allows for both horizontal and vertical scaling to meet varying performance demands.
DAX's node architecture consists of a master node for writes and multiple read replica nodes.
DAX supports common DynamoDB operations like get, query, scan, as well as update, delete, and put.
The introduction of Dino Bass, a tool designed to simplify working with DynamoDB and visualize data.
Caching basics explained, including the concept of a cache as a data storage layer containing a subset of data.
The importance of caching for optimizing database operations and reducing round-trip costs.
DAX's use case suitability for applications with consistent or burst-like traffic on the same set of keys.
Limitations of DAX, including eventual consistency and scenarios where data in the cache may be stale.
Best practices for using DAX, emphasizing the importance of understanding eventual consistency implications.
The recommendation to monitor and scale DAX nodes based on workload to optimize performance and cost.
Potential issues with sharing a DAX cluster across multiple tables and the risk of cascading failures.
The advice to use DynamoDB as the source of truth and not rely solely on DAX for critical data operations.
An overview of how DAX works internally, including the item cache and query cache mechanisms.
The problem of serving stale data from the query cache due to delays in updating the item cache.
The conclusion emphasizing the need for fault tolerance and delivering the best experience for customers when using DAX.
Transcripts
hello everyone this is the video where I
tell you about dynamo DB accelerator
which is also known as Dax and I got to
tell you I'm really super excited about
this video because I've used Dax quite a
bit in the past so I'm quite familiar
with some of the low-level details that
I'm very excited to share with you today
so this video is gonna consist of an
overview of what Dax is a small talk
about what use cases Dax is suitable for
followed by an overview of how it works
than a deep dive on how it works and
finally some talk about best practices
but before I get into that I just
briefly want to mention a neat tool I
came across when working with dynamo DB
and the tool it's called Dino bass Dino
bass is an application that's devoted to
making your life easier when working
with dynamo DB how it includes features
that allow you to perform some normally
tedious dynamo tasks as well as
visualize your data in a pretty
intuitive way and Dino bass has a seven
day free trial so I recommend you try it
out to see if it's right for you you can
find a link in the description section
below so now getting into the meat of
the video I really want to just jump
right into Dax but before we do that I
think we need to set the stage and
quickly talk about what caching is at
its most basic level so first let's talk
about what is a cache why is it useful
so a cache is in its most basic form
it's just a data storage layer that
contains a subset of your data so
imagine you have a country database
where you have country IDs and then some
details about those country objects
typically when we try to retrieve data
from a database there's a round-trip
cost of making that connection to the
database making the query and finally
getting those results back caching is an
optimization technique that allows for
duplication of this data and that
duplicated data sits in memory typically
on a local machine and instead of
reading that data from the database
itself your application will have
business logic to first attempt to read
it from your in-memory cache before you
go and get it from the database so it
essentially serves as a mechanism to
save a trip to the database and deliver
some quicker performance for your get
operations so like I mentioned a cache
entry is usually stored in memory
to quickly retrieve and serve content
and caches can be local or remote you
can have just a local cache that's
serving memory directly our that machine
you can have a remote cache which is
something more similar to how Docs works
where you have multiple nodes that are
working as a unit to act as a cache and
we're gonna get more into those details
later so Before we jump into Dax
specifically let's just go over again
how this works
so say for instance you have an ojs app
and that's hosted on an ec2 machine in
AWS and you have requests that are
coming in for a country ID for instance
what your application would do is first
check your cache for the country ID
that's being queried on if the item is
in the cache it would serve it right
back right away if the item was not in
the cache then you would go to the
database retrieve that data there and
before you returned the row back to the
caller
you put that row in the cache so that
next time when a retrieval operation
comes in that row will be in the cache
so you don't have to make another trip
to the database they got the record
again finally to return that value back
to your clients so this was a brief
overview about how caches work but the
next logical question is how does Dax
fit into this whole picture
we know that Dax is for optimization we
know DynamoDB is a database
what does Dax do and how does it make my
life easier I'm sure you're all asking
this question now so what is Dax
so Dax is a fully managed highly
available caching layer for dynamodb it
delivers microsecond level performance
and if you've worked with dynamo in the
past you know that a typical level of
performance that you get on a getitem
operation is something in the realm of
five to eight milliseconds I would say
on the lower end and if you're doing
something a bit more bulky with query
operations that are retrieving multiple
rows then maybe like a couple hundred
milliseconds maybe 50 milliseconds if
you're lucky but that's the kind of
performance that you're going to get out
of normal and DynamoDB Dax takes that to
the next level and delivers microsecond
level performance for you so you
definitely get a boost in the
performance department at the cost of
some limitations of
Dax and that's what I want to kind of
talk about in the next few slides here
so Dax is horizontally and vertically
scalable and like I mentioned Dax
consists of nodes and each node is an
ec2 machine and a Dax cluster works
together with these nodes that are in
the cluster to serve some highly
available content that's duplicated
across all of your notes so Dax is
horizontally scalable in the sense that
you can easily add more nodes to your
cluster and it's vertically scalable in
the sense that you can throw more CPU or
more memory or faster performing memory
also at your nodes to improve
performance in the vertical dimension
just keep in mind one node will always
serve as your master which is the ledger
for writes and the other nodes that are
in your cluster will serve as your read
replicas and we'll get more into that in
the next few slides as well so Dax by
default supports get query and scan
operations on the data retrieval side
and update delete and put operations on
the data modification side it also
supports caching for LSI GSI operations
and any range key operations that you're
doing when performing queries it allows
for easy migration from just your
standard vanilla DynamoDB to Dax
typically you just have to switch out
your SDK that you're using and then you
just switch the endpoint to point to the
Dax cluster as opposed to the Dynamo
table and finally like I was mentioning
before your cache is hosted on multiple
nodes in a cluster which are actually
just memory optimized ec2 instances and
the cost model is that you just pay per
node there's no additional costs that
are incurred as a result of using Dax
you just pay for the hardware so now I
want to move on to what applications is
Dax suitable for because like I
mentioned it gives you all these
performance boost it's all does all
these great things for you makes things
really fast so you can do all this
optimization in your application but
unfortunately all this performance boost
does come at a cost and the limitations
that arise out of using Dax sometimes
make it such that certain applications
are suitable candidates so let's talk a
little bit about that so when should you
use it when should you not use it
so you should use it if you have an
access pattern such that you have
consistent or burst like traffic on the
same set of keys ideally those keys
don't change very often so you have
limited write patterns on those keys
also obviously I've kind of mentioned a
lot about performance here so if you do
require that microsecond level response
times then you should definitely look
into using Dax I'm going to get more
into the limitations next thirdly if you
if your use case can tolerate eventual
consistency so what eventual consistency
means in pretty basic terms is that
you're going to have scenarios or cases
when you have data that exists for a
specific key on your DynamoDB table that
is inconsistent with what exists on your
cache so say for instance you have a
dynamodb table where your ID is ABC and
the value is 1 in your dynamic table
that's the rollout as it as it exists
ABC with value 1 it's possible due to
the way that Docs works that if that row
gets modified the modification may not
propagate into the Dax cluster so that
when a query takes place on the Dax
cluster it will potentially be serving
up stale content so this is one of the
limitations that you have to factor in
when you're deciding whether or not you
want to use it because this can cause
some very very difficult to debug
scenarios and if you're not aware of it
from the get-go it'll cause some very
complex bugs in your application and
finally you should use it when your
application is read intensive and not
write intensive like I kind of alluded
to before you have one master with
multiple read replicas if you have a
very read intensive application your
master node will very quickly become
overburdened with traffic and write
intensive applications just don't really
make sense for caches caches are
typically used for applications with a
lot of reads and accesses on your data
with not a lot of writes so so far we've
talked about what caches are what Docs
is a little bit about how it works and
kind of if your use case is suitable for
it now I kind of want to jump into a
little bit of an overview to start about
how
DOX works under the hood followed by a
more comprehensive deep dive and that'll
kind of really show you some of the
low-level details about why eventual
consistency can be problematic for
applications so let's move on now into
how it works from an overview so say you
have a transactions table and this
dynamo table stores credit card
transactions in this case and say you
decide to set up a Dax cluster that's
going to sit in front of your dynamo
table because for whatever reason you
have a very high reed pattern on this
table and your read capacity usage is
starting to get expensive right you
don't want to be paying 10000 read
capacity units on your transactions
table you're looking for something
that's going to be cheaper and also give
you a performance boost so you say okay
let's use a Dax cluster so you set up
your docks cluster in the console and
say we specify three nodes in our Dax
cluster by defaults Dax will assign one
to be the master and the other two nodes
to be read replicas which are going to
contain copies of the masters cache so
behind the scenes any rights that occur
will automatically first go to the
master and then propagate their data
into the read replicas so you have this
very natural sharing of information
that's going on between the nodes in
your cluster so Dax will also set up a
load balancer with an endpoint so that
whenever a request comes in it's kind of
a round-robin approach into determining
which node is going to actually serve
the request could be your master it
could be one of the other two reed
replicas so say for instance if a
request comes in to your cache and say
it's a read operation so you're trying
to find maybe
transaction ID t1 let's just write that
down you're looking for t1 in your
transactions table how is that going to
work so you query t1 against the Dax
endpoint you're gonna check the cache to
see if t1 exists in the cache cluster
and the case that it does you just
return it back to the client right away
in the case that it doesn't you need to
go and retrieve that data from your
transactions table so it'll go and query
your dynamo DB database retrieve that
data insert it into your docks
so that it's available for our next
query that comes in before finally
returning it back to your clients so
that's how it works from the read
perspective we had two operations we had
what we call a cache miss when we found
out that the entry didn't exist in the
cache and then we had to go and retrieve
it from the source of truth before we
populated it so that was the read flow
so how does it work for the right flow
so for the right flow if there's a right
to your DynamoDB table that write will
be transactional in nature your first
write will occur on the Dynamo table
itself the second step to be performed
is to write that same row into your
cache so you get that data primed in
your cache because in all likelihood if
you're writing an item to your table
it's very likely that you're gonna read
that item from that table shortly
thereafter so that's the right flow of
how data gets into the cache when data
is modified in the source of truth so
that was a quick overview from a
high-level perspective
now let's talk about the low-level
implementation of Dax and how it works
from the most basic levels so how it
works from the advanced perspective so
again we have our transactions table and
say we decide to set up our cluster
again and this is kind of a zoomed in
view of a Dax cluster say we're just
looking at a particular node say it's
our master node in this case just to
make our lives a little bit simpler so
behind the scene Dax will maintain two
sets of internal caches that are shared
across your Dax cluster so the first one
is called an item cache and the item
caches responsibility is to store
mappings of keys to values so your keys
being the ID of the item and the value
being the item itself
so if you add t1 that's the key in this
example and the contents of that row say
it's a purchase transaction and it's a
$50 value and it took place on a certain
date all that metadata is the value so
we have a mapping between the key which
was t1 and the value or the object which
is a transaction in this case so the
intention of this cache is to maintain
this mapping whenever updates occur on
your
table so say for instance if a request
comes in it's a get item API and it's
pointed to your docs cluster say our
we're using c1 here that's our partition
key and our range key is date we will
first like I mentioned before attempt to
see if it's in the item cache if it's in
the cache we return right away if it's
not we go and look up that item in the
dynamo table before we finally store it
in our item cache with the c1 and date
hashed as the key and the actual value
stored alongside that hash key so that's
the item caches responsibility so
anytime any get item operations occur
they're gonna always look at the item
cache and any time any modifications
occurred to the items on your table
they're gonna be transactionally updated
into your item cache so the next time a
lookup occurs that value will be sitting
in your item cache so the second cache
that occurs is what's called the query
cache and the query cache is what drives
as you would expect your query
operations but it works in a similar but
also a very different way so the query
cache consists of an a mapping between
an ID and a list of objects because as
we know when we submit a query we submit
a key that we're interested in and also
a criteria in the range key case maybe
it's your range key is date and you only
want things that are within the last
seven days so how the query cache works
is slightly different so when a request
comes in with this combination say c1
plus a date criteria the same flow of
operations will happen will first look
into the cache see if we have this
combination of criteria ever queried for
before if we don't then we'll go and
retrieve that data and it finally will
store that data inside the cache for
later use so if someone calls again
within the next 5 minutes or so then
we'll just retrieve from the cache a B
and C and serve up that data very very
quickly so obviously with this
implementation you get very exceptional
performance you don't have to actually
perform a query on your dynamo table
instead you're just looking in a cache
and it's an over the 1 lookup so you get
that data
back right away but I've encouraged you
to do a thought exercise here about why
this is a problem and maybe you want to
pause it to think about it a little bit
because these rows right they're
replicated a b and c they're replicated
in the query cache the a is also
replicated in the item cache and then we
have a transactions database which is
the authority on this data so I want you
to pause for a second if you can try to
think of a scenario when you update
items in this table problems with what
data you serve back so maybe you want to
take a pause take a minute or so to
think about that right now so hopefully
you thought about this problem and let's
walk through why this set up is an issue
and why you should probably never be
using query caches so think about this
case write the set up that I've already
described to you we had a previous
lookup where we did see 1 and 8 and we
retrieved item aim okay
we've already recognized the fact that
if a update occurs on the Dynamo table
there is a delay between when that
update is in the Dynamo table and when
that update is reflected in the item
cache right so that's problem number one
there's an inherent delay between these
two tables that problem is enhanced in
the query cache scenario because what
query cache is do is they cache a
criteria and a list of results so if any
of these results if a B or C changes
while this row exists in your query
cache then the next time a query comes
in for C one plus this specific criteria
this data may have already changed on
the DynamoDB table and you will never
know it so this is part of the reason
that caches on the query side are very
very ill-advised you have the very high
likelihood to serve up stale data so be
very very careful about working with
caches when you're using queries so this
was a more in-depth look about how it
works behind the scenes now I kind of
want to go into best practices things
you should know about when you're using
Dax or maybe you're thinking about using
it in your next project so the first one
is directly tied to my previous point
and that's to
don't cache queries unless you
understand the implications so
understand that you may have
inconsistent data if you're using your
dynamo table as a source of truth that
is for some very mission critical
information maybe it's like credit card
transactions then you don't want to be
using query caches you may not even want
to use caches in general since by
definition they introduced eventual
consistency a second one is from the
monitoring perspective and insta monitor
and scale your notes accordingly based
on workload as you know you're using ec2
machines you're paying for that
always-on functionality I don't think
there's auto scaling for Docs yet but if
there is there'll be a great new feature
but as a result you really need to
monitor your usage pattern I would
suggest starting with the lowest level
type of hardware see if that fits your
use case and then maybe you can add more
nodes to serve as read replicas or beef
up your replicas to be more intensive so
they can serve more traffic and the
third point is to be aware of single
point of failures if you decide to share
your cluster across multiple tables this
is a kind of a neat feature you can sit
you can share your cluster across
multiple DynamoDB tables so you can
store all amounts of data in there and
they could be totally unrelated the
problem with this is that if you're
sharing this across different services
or different use cases it's possible
that a read or write access pattern to
one table can cause a cascading failure
in your cache that can all of a sudden
start impacting your other application
that happens to also use the cache but a
different section of the cache
however if it starts failing it doesn't
matter failures failure there's no
section so failure there's no partition
you know failure it's either up or down
right so in this case you can have
certain parts of your application
impacting other parts of your
application so just be aware of doing
this if you decide to take this kind of
shortcut and the final one which has to
do with eventual consistency is when in
doubt use your dynamo to be record as
the source of truth don't perform
operations where you read from the docs
cluster use it as a source of truth and
then write to your dynamo table like we
said there's eventual consistency so the
likelihood of that data being out of
date at some point is no I don't want to
say hi but it's
possible so keep that in mind when
you're designing your application be
fault tolerant but also try to deliver
the best experience possible for your
customers
so hopefully you like this video I have
many more on dynamodb including this one
on the right here about DynamoDB schema
design also please don't forget to Like
and subscribe so that you don't miss out
on next week's video thanks so much
folks and I'll see you next time
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