Introduction
Indexes are an important a part of correct knowledge modeling for all databases, and DynamoDB is not any exception. DynamoDB’s secondary indexes are a robust device for enabling new entry patterns in your knowledge.
On this publish, we’ll have 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 remedy. Then, we’ll have a look at some sensible ideas for utilizing secondary indexes successfully. Lastly, we’ll shut with some ideas on when you must use secondary indexes and when you must search for different options.
Let’s get began.
What’s DynamoDB, and what are DynamoDB secondary indexes?
Earlier than we get into use instances and greatest practices for secondary indexes, we must always first perceive what DynamoDB secondary indexes are. And to do this, we must always perceive a bit about how DynamoDB works.
This assumes some fundamental understanding of DynamoDB. We’ll cowl the essential factors you want to know to grasp secondary indexes, however for those who’re new to DynamoDB, you might need to begin with a extra fundamental introduction.
The Naked Minimal you Have to Learn about DynamoDB
DynamoDB is a novel 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 buying cart, liking a video, or including a touch upon Reddit. In that means, it could possibly deal with related 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 desires to have the identical latency in your OLTP-like requests. It is a large 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 you want to perceive to make use of it successfully.
First, DynamoDB horizontally scales your databases by spreading your knowledge throughout a number of partitions beneath the hood. These partitions will not be seen to you as a person, however they’re on the core of how DynamoDB works. You’ll specify a main key in your desk (both a single factor, referred to as a ‘partition key’, or a mix of a partition key and a kind key), and DynamoDB will use that main key to find out which partition your knowledge 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 — typically 10GB or much less — to allow them to be moved, break up, replicated, and in any other case managed independently.
Horizontal scalability by way of sharding is attention-grabbing however is not at all 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 main key to entry your knowledge. Relatively than utilizing a question planner that interprets your requests right into a sequence of queries, DynamoDB forces you to make use of your main key to entry your knowledge. You might be basically getting a immediately addressable index in your knowledge.
The API for DynamoDB displays this. There are a sequence of operations on particular person objects (GetItem, PutItem, UpdateItem, DeleteItem) that help you learn, write, and delete particular person objects. Moreover, there’s a Question operation that means that you can retrieve a number of objects with the identical partition key. When you have a desk with a composite main key, objects with the identical partition key can be grouped collectively on the identical partition. They are going to be ordered in accordance with the kind key, permitting you to deal with patterns like “Fetch the latest Orders for a Person” or “Fetch the final 10 Sensor Readings for an IoT Gadget”.
For instance, we could 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 main key with a partition key of ‘Group’ and a kind 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 have a look at secondary indexes. One of the best ways to grasp the necessity for secondary indexes is to grasp the issue they remedy. We have seen how DynamoDB partitions your knowledge in accordance with your main key and the way it pushes you to make use of the first key to entry your knowledge. That is all nicely and good for some entry patterns, however what if you want to entry your knowledge differently?
In our instance above, we had a desk of customers that we accessed by their group and username. Nonetheless, we may additionally must fetch a single person by their e-mail tackle. This sample does not match with the first key entry sample that DynamoDB pushes us in direction of. As a result of our desk is partitioned by completely different attributes, there’s not a transparent technique to entry our knowledge in the way in which we wish. We may do a full desk scan, however that is sluggish and inefficient. We may duplicate our knowledge right into a separate desk with a distinct main key, however that provides complexity.
That is the place secondary indexes are available. A secondary index is mainly a totally managed copy of your knowledge with a distinct main 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 part on this part applies to world secondary indexes. DynamoDB additionally gives native secondary indexes, that are a bit completely different. In nearly all instances, you will have 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 knowledge, it has simply been reorganized with a distinct main key. Now, we will effectively search for a person by their e-mail tackle.
In some methods, that is similar to an index in different databases. Each present an information construction that’s optimized for lookups on a specific attribute. However DynamoDB’s secondary indexes are completely different in a number of key methods.
First, and most significantly, DynamoDB’s indexes reside on solely completely different partitions than your most important desk. DynamoDB desires each lookup to be environment friendly and predictable, and it desires to offer linear horizontal scaling. To do that, it must reshard your knowledge by the attributes you will use to question it.
In different distributed databases, they typically do not reshard your knowledge for the secondary index. They’re going to often simply preserve the secondary index for all knowledge on the shard. Nonetheless, in case your indexes do not use the shard key, you are shedding among the advantages of horizontally scaling your knowledge as a question with out the shard key might want to do a scatter-gather operation throughout all shards to search 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 your entire 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 document within the index, the database will then must go fetch the complete merchandise. As a result of DynamoDB’s secondary indexes are on completely different nodes than the primary desk, they need to keep away from a community hop again to the unique merchandise. As a substitute, you will copy as a lot knowledge 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’ll be able to’t write on to a secondary index. Relatively, you’ll write to your most important 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 whole write prices in your desk.
Ideas for utilizing secondary indexes
Now that we perceive what secondary indexes are and the way they work, let’s speak about the best way to use them successfully. Secondary indexes are a robust device, however they are often misused. Listed below are some ideas 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 you must 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 primary desk. This leads to additional value and further latency, and you may usually keep away from it with some upfront planning.
In the event you’ve learn something about DynamoDB knowledge modeling, you most likely know that you must 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, you must 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 easy, ideally with out utilizing DynamoDB Transactions or utilizing a read-modify-write sample that could possibly be topic to race circumstances.
As you’re employed by these, you will typically discover that there is a ‘main’ technique to establish your merchandise that matches up along with your write patterns. This can find yourself being your main key. Then, including in extra, secondary learn patterns is straightforward with secondary indexes.
In our Customers instance earlier than, each Person request will probably embody the Group and the Username. This can enable me to search for the person Person document in addition to authorize particular actions by the Person. The e-mail tackle 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, and so they 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 have a look at conditions the place it applies.
DynamoDB means that you can 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 will must delete the outdated merchandise and create a brand new one. This two-step course of is slower and dear. Typically you will must learn the unique merchandise first, then use a transaction to delete the unique merchandise and create a brand new one in the identical request.
However, when you’ve got this mutable worth within the main key of a secondary index, then DynamoDB will deal with this delete + create course of for you throughout replication. You possibly can problem a easy UpdateItem request to vary the worth, and DynamoDB will deal with the remaining.
I see this sample come up in two most important conditions. The primary, and most typical, is when you have got a mutable attribute that you simply need to type on. The canonical examples listed here are a leaderboard for a recreation the place individuals are frequently racking up factors, or for a frequently updating checklist 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’ll be able to type your information by ‘final modified’.
A second sample the place this comes up is when you have got a mutable attribute that you simply need to filter on. Right here, you’ll be able to 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 type key to permit exact-match filtering. Because the merchandise adjustments standing, you’ll be able to replace the standing attribute and lean on DynamoDB to group the objects appropriately in your secondary index.
In each of those conditions, shifting this mutable attribute to your secondary index will prevent money and time. You may 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, word that this sample matches nicely with the earlier tip. It is unlikely you’ll establish an merchandise for writing primarily based on the mutable attribute like their earlier rating, their earlier standing, or the final time they had been up to date. Relatively, you will replace by a extra persistent worth, just like the person’s ID, the order ID, or the file’s ID. Then, you will use the secondary index to type and filter primarily based on the mutable attribute.
Keep away from the ‘fats’ partition
We noticed above that DynamoDB divides your knowledge into partitions primarily based on the first key. DynamoDB goals to maintain these partitions small — 10GB or much less — and you must purpose to unfold requests throughout your partitions to get the advantages of DynamoDB’s scalability.
This typically means you must use a high-cardinality worth in your partition key. Consider one thing like a username, an order ID, or a sensor ID. There are massive numbers of values for these attributes, and DynamoDB can unfold the site visitors throughout your partitions.
Typically, I see folks perceive this precept of their most important desk, however then utterly overlook about it of their secondary indexes. Typically, they need ordering throughout your entire desk for a kind of merchandise. In the event that they need to retrieve customers alphabetically, they’re going to use a secondary index the place all customers have USERS because the partition key and the username as the kind key. Or, if they need ordering of the latest orders in an ecommerce retailer, they’re going to use a secondary index the place all orders have ORDERS because the partition key and the timestamp as the kind key.
This sample can work for small-traffic functions the place you will not come near the DynamoDB partition throughput limits, but it surely’s a harmful sample for a high traffic utility. Your whole site visitors could also be funneled to a single bodily partition, and you may shortly hit the write throughput limits for that partition.
Additional, and most dangerously, this may trigger issues in your most important 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 most important desk.
That is designed that can assist you — DynamoDB desires to restrict the staleness of your secondary index, so it should forestall you from a secondary index with a considerable amount of lag. Nonetheless, it may be a stunning scenario that pops up while you’re least anticipating it.
Use sparse indexes as a world filter
Folks usually consider secondary indexes as a technique to replicate all of their knowledge with a brand new main key. Nonetheless, you do not want all your knowledge to finish up in a secondary index. When you 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 knowledge. The canonical instance I exploit for it is a message inbox. In your most important desk, you may retailer all of the messages for a specific person ordered by the point they had been created.
However for those who’re like me, you have got loads of messages in your inbox. Additional, you may deal with unread messages as a ‘todo’ checklist, like little reminders to get again to somebody. Accordingly, I often solely need to see the unread messages in my inbox.
You may use your secondary index to offer this world filter the place unread == true. Maybe your secondary index partition secret is one thing like ${userId}#UNREAD, and the kind secret is the timestamp of the message. If you create the message initially, it should embody the secondary index partition key worth and thus can be replicated to the unread messages secondary index. Later, when a person reads the message, you’ll be able to 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 exploit this trick on a regular basis, and it is remarkably efficient. Additional, a sparse index will prevent cash. Any updates to learn messages won’t be replicated to the secondary index, and you may save on write prices.
Slim your secondary index projections to scale back index measurement and/or writes
For our final tip, let’s take the earlier level a bit of additional. We simply noticed that DynamoDB will not embody an merchandise in your secondary index if the merchandise does not have the first key parts for the index. This trick can be utilized for not solely main key parts but additionally for non-key attributes within the knowledge!
If you create a secondary index, you’ll be able to specify which attributes from the primary desk you need to embody within the secondary index. That is referred to as the projection of the index. You possibly can select to incorporate all attributes from the primary 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 expensive mistake. Keep in mind that each write to your most important desk that adjustments the worth of a projected attribute can be replicated to your secondary index. A single secondary index with full projection successfully doubles the write prices in your desk. Every extra 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 primarily based on the scale of your merchandise. Every 1KB of information written to your desk makes use of a WCU. In the event you’re copying a 4KB merchandise to your secondary index, you will be paying the complete 4 WCUs on each your most important desk and your secondary index.
Thus, there are two methods that you could lower your expenses by narrowing your secondary index projections. First, you’ll be able to keep away from sure writes altogether. When you 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 decreasing the scale of the merchandise that’s replicated.
This could be a tough steadiness to get proper. Secondary index projections will not be alterable after the index is created. In the event you discover that you simply want extra attributes in your secondary index, you will must create a brand new index with the brand new projection after which delete the outdated index.
Must you 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 elementary query — do you have to use a secondary index in any respect?
As we have seen, secondary indexes enable you to entry your knowledge 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 while 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 excited about different approaches.
To deliver this dwelling, let us take a look at two conditions the place secondary indexes won’t make sense.
Numerous filterable attributes in small merchandise collections
With DynamoDB, you typically need your main keys to do your filtering for you. It irks me a bit of every time I exploit 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 may need to over-read your knowledge after which filter in your utility.
The most typical place you will see that is while you need to present loads of completely different filters in your knowledge in your customers, however the related knowledge set is bounded.
Consider a exercise tracker. You may need to enable customers to filter on loads of attributes, akin 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. Relatively than placing indexes on all of those attributes, you’ll be able to simply fetch all of the person’s exercises after which filter in your utility.
That is the place I like to recommend doing the maths. DynamoDB makes it straightforward to calculate these two choices and get a way of which one will work higher in your utility.
Numerous filterable attributes in massive merchandise collections
Let’s change our scenario a bit — what if our merchandise assortment is massive? What if we’re constructing a exercise tracker for a fitness center, and we need to enable the fitness center proprietor to filter on all the attributes we talked about above for all of the customers within the fitness center?
This adjustments the scenario. Now we’re speaking about a whole lot and even hundreds of customers, every with a whole lot or hundreds of exercises. It will not make sense to over-read your entire 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 identified entry patterns the place you’ll be able to depend on the related filters being current. If we wish our fitness center proprietor to have the ability to filter on a wide range of attributes, all of that are non-compulsory, we might must create a lot of indexes to make this work.
We talked concerning 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 take 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 is going to end in loads of forwards and backwards along with your utility, together with some advanced utility logic to determine it out.
When I’ve these kind of issues, I typically search for a device higher suited to this use case. Rockset and Elasticsearch are my go-to suggestions right here for offering versatile, secondary-index-like filtering throughout your dataset.
Conclusion
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 the best way to use secondary indexes successfully and to study their particular quirks. Lastly, we checked out how to consider secondary indexes to see when you must use different approaches.
Secondary indexes are a robust device in your DynamoDB toolbox, however they don’t seem to be a silver bullet. As with all DynamoDB knowledge modeling, ensure you rigorously contemplate your entry patterns and depend the prices earlier than you leap in.
Study extra about how you should utilize Rockset for secondary-index-like filtering in Alex DeBrie’s weblog DynamoDB Filtering and Aggregation Queries Utilizing SQL on Rockset.