DynamoDB Secondary Indexes | Rockset


Indexes are a vital a part of correct information modeling for all databases, and DynamoDB isn’t any exception. DynamoDB’s secondary indexes are a strong software for enabling new entry patterns on your information.

On this publish, we’ll take a look at DynamoDB secondary indexes. First, we’ll begin with some conceptual factors about how to consider DynamoDB and the issues that secondary indexes clear up. Then, we’ll take a look at some sensible suggestions for utilizing secondary indexes successfully. Lastly, we’ll shut with some ideas on when it’s best to use secondary indexes and when it’s best to search for different options.

Let’s get began.

What’s DynamoDB, and what are DynamoDB secondary indexes?

Earlier than we get into use circumstances and greatest practices for secondary indexes, we must always first perceive what DynamoDB secondary indexes are. And to try this, we must always perceive a bit about how DynamoDB works.

This assumes some fundamental understanding of DynamoDB. We’ll cowl the essential factors it’s essential to know to grasp secondary indexes, however when you’re new to DynamoDB, you might need to begin with a extra fundamental introduction.

The Naked Minimal you Must Learn about DynamoDB

DynamoDB is a singular database. It is designed for OLTP workloads, that means it is nice for dealing with a excessive quantity of small operations — consider issues like including an merchandise to a purchasing cart, liking a video, or including a touch upon Reddit. In that means, it could possibly deal with comparable functions as different databases you might need used, like MySQL, PostgreSQL, MongoDB, or Cassandra.

DynamoDB’s key promise is its assure of constant efficiency at any scale. Whether or not your desk has 1 megabyte of information or 1 petabyte of information, DynamoDB needs to have the identical latency on your OLTP-like requests. This can be a massive deal — many databases will see lowered efficiency as you enhance the quantity of information or the variety of concurrent requests. Nonetheless, offering these ensures requires some tradeoffs, and DynamoDB has some distinctive traits that it’s essential to perceive to make use of it successfully.

First, DynamoDB horizontally scales your databases by spreading your information throughout a number of partitions below the hood. These partitions aren’t seen to you as a person, however they’re on the core of how DynamoDB works. You’ll specify a major key on your desk (both a single ingredient, referred to as a ‘partition key’, or a mix of a partition key and a form key), and DynamoDB will use that major key to find out which partition your information lives on. Any request you make will undergo a request router that can decide which partition ought to deal with the request. These partitions are small — usually 10GB or much less — to allow them to be moved, break up, replicated, and in any other case managed independently.

Screenshot 2024-02-22 at 11.36.22 AM

Horizontal scalability through sharding is attention-grabbing however is in no way distinctive to DynamoDB. Many different databases — each relational and non-relational — use sharding to horizontally scale. Nonetheless, what is distinctive to DynamoDB is the way it forces you to make use of your major key to entry your information. Fairly than utilizing a question planner that interprets your requests right into a collection of queries, DynamoDB forces you to make use of your major key to entry your information. You’re primarily getting a straight addressable index on your information.

The API for DynamoDB displays this. There are a collection of operations on particular person objects (GetItem, PutItem, UpdateItem, DeleteItem) that permit you to learn, write, and delete particular person objects. Moreover, there’s a Question operation that permits you to retrieve a number of objects with the identical partition key. You probably have a desk with a composite major key, objects with the identical partition key will likely be grouped collectively on the identical partition. They are going to be ordered in keeping with the type key, permitting you to deal with patterns like “Fetch the newest Orders for a Person” or “Fetch the final 10 Sensor Readings for an IoT System”.

For instance, lets say a SaaS utility that has a desk of Customers. All Customers belong to a single Group. We’d have a desk that appears as follows:


We’re utilizing a composite major key with a partition key of ‘Group’ and a form key of ‘Username’. This permits us to do operations to fetch or replace a person Person by offering their Group and Username. We will additionally fetch all the Customers for a single Group by offering simply the Group to a Question operation.

What are secondary indexes, and the way do they work

With some fundamentals in thoughts, let’s now take a look at secondary indexes. One of the best ways to grasp the necessity for secondary indexes is to grasp the issue they clear up. We have seen how DynamoDB partitions your information in keeping with your major key and the way it pushes you to make use of the first key to entry your information. That is all nicely and good for some entry patterns, however what if it’s essential to entry your information differently?

In our instance above, we had a desk of customers that we accessed by their group and username. Nonetheless, we might also have to fetch a single person by their e-mail handle. This sample would not match with the first key entry sample that DynamoDB pushes us in the direction of. As a result of our desk is partitioned by completely different attributes, there’s not a transparent method to entry our information in the best way we wish. We may do a full desk scan, however that is sluggish and inefficient. We may duplicate our information right into a separate desk with a special major key, however that provides complexity.

That is the place secondary indexes are available in. A secondary index is mainly a totally managed copy of your information with a special major key. You’ll specify a secondary index in your desk by declaring the first key for the index. As writes come into your desk, DynamoDB will robotically replicate the information to your secondary index.

Be aware: Every little thing on this part applies to international secondary indexes. DynamoDB additionally gives native secondary indexes, that are a bit completely different. In virtually all circumstances, you want a world secondary index. For extra particulars on the variations, take a look at this text on selecting a world or native secondary index.

On this case, we’ll add a secondary index to our desk with a partition key of “E mail”. The secondary index will look as follows:


Discover that this is similar information, it has simply been reorganized with a special major key. Now, we are able to effectively lookup a person by their e-mail handle.

In some methods, that is similar to an index in different databases. Each present an information construction that’s optimized for lookups on a selected attribute. However DynamoDB’s secondary indexes are completely different in a number of key methods.

First, and most significantly, DynamoDB’s indexes dwell on totally completely different partitions than your essential desk. DynamoDB needs each lookup to be environment friendly and predictable, and it needs to supply linear horizontal scaling. To do that, it must reshard your information by the attributes you may use to question it.

Screenshot 2024-02-22 at 11.37.21 AM

In different distributed databases, they typically do not reshard your information for the secondary index. They will normally simply keep the secondary index for all information on the shard. Nonetheless, in case your indexes do not use the shard key, you are shedding a number of the advantages of horizontally scaling your information as a question with out the shard key might want to do a scatter-gather operation throughout all shards to seek out the information you are on the lookout for.

A second means that DynamoDB’s secondary indexes are completely different is that they (usually) copy the complete merchandise to the secondary index. For indexes on a relational database, the index will usually include a pointer to the first key of the merchandise being listed. After finding a related report within the index, the database will then have to go fetch the total merchandise. As a result of DynamoDB’s secondary indexes are on completely different nodes than the principle desk, they need to keep away from a community hop again to the unique merchandise. As an alternative, you may copy as a lot information as you want into the secondary index to deal with your learn.

Secondary indexes in DynamoDB are highly effective, however they’ve some limitations. First off, they’re read-only — you may’t write on to a secondary index. Fairly, you’ll write to your essential desk, and DynamoDB will deal with the replication to your secondary index. Second, you might be charged for the write operations to your secondary indexes. Thus, including a secondary index to your desk will usually double the entire write prices on your desk.

Suggestions for utilizing secondary indexes

Now that we perceive what secondary indexes are and the way they work, let’s speak about methods to use them successfully. Secondary indexes are a strong software, however they are often misused. Listed here are some suggestions for utilizing secondary indexes successfully.

Attempt to have read-only patterns on secondary indexes

The primary tip appears apparent — secondary indexes can solely be used for reads, so it’s best to purpose to have read-only patterns in your secondary indexes! And but, I see this error on a regular basis. Builders will first learn from a secondary index, then write to the principle desk. This ends in further value and further latency, and you may usually keep away from it with some upfront planning.

For those who’ve learn something about DynamoDB information modeling, you most likely know that it’s best to consider your entry patterns first. It isn’t like a relational database the place you first design normalized tables after which write queries to affix them collectively. In DynamoDB, it’s best to take into consideration the actions your utility will take, after which design your tables and indexes to help these actions.

When designing my desk, I like to begin with the write-based entry patterns first. With my writes, I am usually sustaining some kind of constraint — uniqueness on a username or a most variety of members in a gaggle. I need to design my desk in a means that makes this simple, ideally with out utilizing DynamoDB Transactions or utilizing a read-modify-write sample that may very well be topic to race situations.

As you’re employed by means of these, you may usually discover that there is a ‘major’ method to determine your merchandise that matches up together with your write patterns. This may find yourself being your major key. Then, including in further, secondary learn patterns is straightforward with secondary indexes.

In our Customers instance earlier than, each Person request will doubtless embrace the Group and the Username. This may enable me to lookup the person Person report in addition to authorize particular actions by the Person. The e-mail handle lookup could also be for much less outstanding entry patterns, like a ‘forgot password’ move or a ‘seek for a person’ move. These are read-only patterns, they usually match nicely with a secondary index.

Use secondary indexes when your keys are mutable

A second tip for utilizing secondary indexes is to make use of them for mutable values in your entry patterns. Let’s first perceive the reasoning behind it, after which take a look at conditions the place it applies.

DynamoDB permits you to replace an current merchandise with the UpdateItem
operation. Nonetheless, you can’t change the first key of an merchandise in an replace. The first secret is the distinctive identifier for an merchandise, and altering the first secret is mainly creating a brand new merchandise. If you wish to change the first key of an current merchandise, you may have to delete the previous merchandise and create a brand new one. This two-step course of is slower and expensive. Typically you may have to learn the unique merchandise first, then use a transaction to delete the unique merchandise and create a brand new one in the identical request.

Then again, when you’ve got this mutable worth within the major key of a secondary index, then DynamoDB will deal with this delete + create course of for you throughout replication. You possibly can subject a easy UpdateItem request to vary the worth, and DynamoDB will deal with the remainder.

I see this sample come up in two essential conditions. The primary, and commonest, is when you have got a mutable attribute that you just need to kind on. The canonical examples listed below are a leaderboard for a recreation the place persons are regularly racking up factors, or for a regularly updating record of things the place you need to show essentially the most just lately up to date objects first. Consider one thing like Google Drive, the place you may kind your information by ‘final modified’.

A second sample the place this comes up is when you have got a mutable attribute that you just need to filter on. Right here, you may consider an ecommerce retailer with a historical past of orders for a person. You might need to enable the person to filter their orders by standing — present me all my orders which might be ‘shipped’ or ‘delivered’. You possibly can construct this into your partition key or the start of your kind key to permit exact-match filtering. Because the merchandise modifications standing, you may replace the standing attribute and lean on DynamoDB to group the objects accurately in your secondary index.

In each of those conditions, transferring this mutable attribute to your secondary index will prevent money and time. You will save time by avoiding the read-modify-write sample, and you may lower your expenses by avoiding the additional write prices of the transaction.

Moreover, be aware that this sample matches nicely with the earlier tip. It is unlikely you’ll determine an merchandise for writing based mostly on the mutable attribute like their earlier rating, their earlier standing, or the final time they had been up to date. Fairly, you may replace by a extra persistent worth, just like the person’s ID, the order ID, or the file’s ID. Then, you may use the secondary index to kind and filter based mostly on the mutable attribute.

Keep away from the ‘fats’ partition

We noticed above that DynamoDB divides your information into partitions based mostly on the first key. DynamoDB goals to maintain these partitions small — 10GB or much less — and it’s best to purpose to unfold requests throughout your partitions to get the advantages of DynamoDB’s scalability.

This usually means it’s best to use a high-cardinality worth in your partition key. Consider one thing like a username, an order ID, or a sensor ID. There are giant numbers of values for these attributes, and DynamoDB can unfold the visitors throughout your partitions.

Typically, I see folks perceive this precept of their essential desk, however then fully neglect about it of their secondary indexes. Typically, they need ordering throughout the complete desk for a sort of merchandise. In the event that they need to retrieve customers alphabetically, they will use a secondary index the place all customers have USERS because the partition key and the username as the type key. Or, if they need ordering of the newest orders in an ecommerce retailer, they will use a secondary index the place all orders have ORDERS because the partition key and the timestamp as the type key.

This sample can work for small-traffic functions the place you will not come near the DynamoDB partition throughput limits, nevertheless it’s a harmful sample for a high traffic utility. Your entire visitors could also be funneled to a single bodily partition, and you may rapidly hit the write throughput limits for that partition.

Additional, and most dangerously, this could trigger issues on your essential desk. In case your secondary index is getting write throttled throughout replication, the replication queue will again up. If this queue backs up an excessive amount of, DynamoDB will begin rejecting writes in your essential desk.

That is designed that can assist you — DynamoDB needs to restrict the staleness of your secondary index, so it would stop you from a secondary index with a considerable amount of lag. Nonetheless, it may be a shocking scenario that pops up whenever you’re least anticipating it.

Use sparse indexes as a world filter

Folks usually consider secondary indexes as a method to replicate all of their information with a brand new major key. Nonetheless, you do not want your entire information to finish up in a secondary index. You probably have an merchandise that does not match the index’s key schema, it will not be replicated to the index.

This may be actually helpful for offering a world filter in your information. The canonical instance I take advantage of for this can be a message inbox. In your essential desk, you would possibly retailer all of the messages for a selected person ordered by the point they had been created.

However when you’re like me, you have got a variety of messages in your inbox. Additional, you would possibly deal with unread messages as a ‘todo’ record, like little reminders to get again to somebody. Accordingly, I normally solely need to see the unread messages in my inbox.

You would use your secondary index to supply this international filter the place unread == true. Maybe your secondary index partition secret is one thing like ${userId}#UNREAD, and the type secret is the timestamp of the message. Whenever you create the message initially, it would embrace the secondary index partition key worth and thus will likely be replicated to the unread messages secondary index. Later, when a person reads the message, you may change the standing to READ and delete the secondary index partition key worth. DynamoDB will then take away it out of your secondary index.

I take advantage of this trick on a regular basis, and it is remarkably efficient. Additional, a sparse index will prevent cash. Any updates to learn messages is not going to be replicated to the secondary index, and you may save on write prices.

Slender your secondary index projections to scale back index measurement and/or writes

For our final tip, let’s take the earlier level a little bit additional. We simply noticed that DynamoDB will not embrace an merchandise in your secondary index if the merchandise would not have the first key parts for the index. This trick can be utilized for not solely major key parts but additionally for non-key attributes within the information!

Whenever you create a secondary index, you may specify which attributes from the principle desk you need to embrace within the secondary index. That is referred to as the projection of the index. You possibly can select to incorporate all attributes from the principle desk, solely the first key attributes, or a subset of the attributes.

Whereas it is tempting to incorporate all attributes in your secondary index, this could be a pricey mistake. Keep in mind that each write to your essential desk that modifications the worth of a projected attribute will likely be replicated to your secondary index. A single secondary index with full projection successfully doubles the write prices on your desk. Every further secondary index will increase your write prices by 1/N + 1, the place N is the variety of secondary indexes earlier than the brand new one.

Moreover, your write prices are calculated based mostly on the scale of your merchandise. Every 1KB of information written to your desk makes use of a WCU. For those who’re copying a 4KB merchandise to your secondary index, you may be paying the total 4 WCUs on each your essential desk and your secondary index.

Thus, there are two methods that you would be able to lower your expenses by narrowing your secondary index projections. First, you may keep away from sure writes altogether. You probably have an replace operation that does not contact any attributes in your secondary index projection, DynamoDB will skip the write to your secondary index. Second, for these writes that do replicate to your secondary index, it can save you cash by lowering the scale of the merchandise that’s replicated.

This could be a tough stability to get proper. Secondary index projections aren’t alterable after the index is created. For those who discover that you just want further attributes in your secondary index, you may have to create a brand new index with the brand new projection after which delete the previous index.

Do you have to use a secondary index?

Now that we have explored some sensible recommendation round secondary indexes, let’s take a step again and ask a extra basic query — must you use a secondary index in any respect?

As we have seen, secondary indexes make it easier to entry your information differently. Nonetheless, this comes at the price of the extra writes. Thus, my rule of thumb for secondary indexes is:

Use secondary indexes when the lowered learn prices outweigh the elevated write prices.

This appears apparent whenever you say it, however it may be counterintuitive as you are modeling. It appears really easy to say “Throw it in a secondary index” with out interested by different approaches.

To deliver this dwelling, let’s take a look at two conditions the place secondary indexes won’t make sense.

A number of filterable attributes in small merchandise collections

With DynamoDB, you usually need your major keys to do your filtering for you. It irks me a little bit each time I take advantage of a Question in DynamoDB however then carry out my very own filtering in my utility — why could not I simply construct that into the first key?

Regardless of my visceral response, there are some conditions the place you would possibly need to over-read your information after which filter in your utility.

The most typical place you may see that is whenever you need to present a variety of completely different filters in your information on your customers, however the related information set is bounded.

Consider a exercise tracker. You would possibly need to enable customers to filter on a variety of attributes, similar to kind of exercise, depth, length, date, and so forth. Nonetheless, the variety of exercises a person has goes to be manageable — even an influence person will take some time to exceed 1000 exercises. Fairly than placing indexes on all of those attributes, you may simply fetch all of the person’s exercises after which filter in your utility.

That is the place I like to recommend doing the mathematics. DynamoDB makes it straightforward to calculate these two choices and get a way of which one will work higher on your utility.

A number of filterable attributes in giant merchandise collections

Let’s change our scenario a bit — what if our merchandise assortment is giant? What if we’re constructing a exercise tracker for a gymnasium, and we need to enable the gymnasium proprietor to filter on all the attributes we talked about above for all of the customers within the gymnasium?

This modifications the scenario. Now we’re speaking about tons of and even hundreds of customers, every with tons of or hundreds of exercises. It will not make sense to over-read the complete merchandise assortment and do post-hoc filtering on the outcomes.

However secondary indexes do not actually make sense right here both. Secondary indexes are good for recognized entry patterns the place you may depend on the related filters being current. If we wish our gymnasium proprietor to have the ability to filter on a wide range of attributes, all of that are elective, we might have to create numerous indexes to make this work.

We talked in regards to the doable downsides of question planners earlier than, however question planners have an upside too. Along with permitting for extra versatile queries, they’ll additionally do issues like index intersections to have a look at partial outcomes from a number of indexes in composing these queries. You are able to do the identical factor with DynamoDB, however it’ll end in a variety of forwards and backwards together with your utility, together with some advanced utility logic to determine it out.

When I’ve these kind of issues, I usually search for a software higher fitted to this use case. Rockset and Elasticsearch are my go-to suggestions right here for offering versatile, secondary-index-like filtering throughout your dataset.


On this publish, we discovered about DynamoDB secondary indexes. First, we checked out some conceptual bits to grasp how DynamoDB works and why secondary indexes are wanted. Then, we reviewed some sensible tricks to perceive methods to use secondary indexes successfully and to study their particular quirks. Lastly, we checked out how to consider secondary indexes to see when it’s best to use different approaches.

Secondary indexes are a strong software in your DynamoDB toolbox, however they are not a silver bullet. As with all DynamoDB information modeling, be sure to fastidiously contemplate your entry patterns and depend the prices earlier than you soar in.

Be taught extra about how you should use Rockset for secondary-index-like filtering in Alex DeBrie’s weblog DynamoDB Filtering and Aggregation Queries Utilizing SQL on Rockset.

Recent Articles

Related Stories

Leave A Reply

Please enter your comment!
Please enter your name here