Introduction
Indexes are an important a part of correct information modeling for all databases, and DynamoDB is not any exception. DynamoDB’s secondary indexes are a strong device for enabling new entry patterns to your information.
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 resolve. Then, we’ll have a look at some sensible ideas for utilizing secondary indexes successfully. Lastly, we’ll shut with some ideas on when it is best to use secondary indexes and when it is 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 do this, we must always perceive a bit about how DynamoDB works.
This assumes some primary understanding of DynamoDB. We’ll cowl the fundamental factors it is advisable to know to grasp secondary indexes, however in case you’re new to DynamoDB, it’s possible you’ll wish to begin with a extra primary introduction.
The Naked Minimal you Must Learn about DynamoDB
DynamoDB is a singular database. It is designed for OLTP workloads, which 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 manner, it will probably deal with comparable purposes as different databases you may 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 to your OLTP-like requests. This can be a large deal — many databases will see decreased 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 is advisable to perceive to make use of it successfully.
First, DynamoDB horizontally scales your databases by spreading your information throughout a number of partitions beneath the hood. These partitions are usually not seen to you as a consumer, however they’re on the core of how DynamoDB works. You’ll specify a main key to your desk (both a single aspect, referred to as a ‘partition key’, or a mixture of a partition key and a kind key), and DynamoDB will use that main key to find out which partition your information lives on. Any request you make will undergo a request router that may decide which partition ought to deal with the request. These partitions are small — usually 10GB or much less — to allow them to be moved, cut up, replicated, and in any other case managed independently.
Horizontal scalability through sharding is fascinating however is under no circumstances 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 information. 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 information. You might be basically getting a immediately addressable index to your information.
The API for DynamoDB displays this. There are a sequence of operations on particular person gadgets (GetItem, PutItem, UpdateItem, DeleteItem) that mean you can learn, write, and delete particular person gadgets. Moreover, there’s a Question operation that lets you retrieve a number of gadgets with the identical partition key. If in case you have a desk with a composite main key, gadgets with the identical partition key will probably be grouped collectively on the identical partition. They are going to be ordered in line with the kind 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 Machine”.
For instance, we could say a SaaS utility that has a desk of Customers. All Customers belong to a single Group. We would 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 enables us to do operations to fetch or replace a person Person by offering their Group and Username. We are able to 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 simplest ways to grasp the necessity for secondary indexes is to grasp the issue they resolve. We have seen how DynamoDB partitions your information in line with your main key and the way it pushes you to make use of the first key to entry your information. That is all properly and good for some entry patterns, however what if it is advisable 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 may must fetch a single consumer by their electronic mail deal with. This sample would not match with the first key entry sample that DynamoDB pushes us in direction of. As a result of our desk is partitioned by totally different attributes, there’s not a transparent strategy to entry our information in the way in which we would like. We may do a full desk scan, however that is gradual and inefficient. We may duplicate our information right into a separate desk with a unique main 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 unique 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 mechanically replicate the information to your secondary index.
Notice: Every part on this part applies to world secondary indexes. DynamoDB additionally offers native secondary indexes, that are a bit totally different. In nearly all circumstances, you want a worldwide secondary index. For extra particulars on the variations, try this text on selecting a worldwide or native secondary index.
On this case, we’ll add a secondary index to our desk with a partition key of “Electronic mail”. The secondary index will look as follows:
Discover that this is identical information, it has simply been reorganized with a unique main key. Now, we will effectively lookup a consumer by their electronic mail deal with.
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 totally different in a number of key methods.
First, and most significantly, DynamoDB’s indexes stay on fully totally different partitions than your essential desk. DynamoDB desires each lookup to be environment friendly and predictable, and it desires to supply linear horizontal scaling. To do that, it must reshard your information by the attributes you may use to question it.
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 dropping among 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 search out the information you are on the lookout for.
A second manner that DynamoDB’s secondary indexes are totally different is that they (usually) copy the whole 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 total merchandise. As a result of DynamoDB’s secondary indexes are on totally different nodes than the primary desk, they wish to keep away from a community hop again to the unique merchandise. As a substitute, 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. Relatively, 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 whole write prices to 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 right way to use them successfully. Secondary indexes are a strong device, however they are often misused. Listed here 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 it is best to intention 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 additional latency, and you may usually keep away from it with some upfront planning.
In the event you’ve learn something about DynamoDB information modeling, you in all probability know that it is 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 is best to take into consideration the actions your utility will take, after which design your tables and indexes to assist these actions.
When designing my desk, I like to start out with the write-based entry patterns first. With my writes, I am usually sustaining some sort of constraint — uniqueness on a username or a most variety of members in a bunch. I wish to design my desk in a manner that makes this simple, ideally with out utilizing DynamoDB Transactions or utilizing a read-modify-write sample that might be topic to race situations.
As you’re employed by these, you may usually discover that there is a ‘main’ strategy to establish your merchandise that matches up together with your write patterns. It will 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 seemingly embody the Group and the Username. It will permit me to lookup the person Person document in addition to authorize particular actions by the Person. The e-mail deal with lookup could also be for much less outstanding entry patterns, like a ‘forgot password’ move or a ‘seek for a consumer’ move. These are read-only patterns, and so they match properly 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 lets you replace an present merchandise with the UpdateItem
operation. Nonetheless, you can not change the first key of an merchandise in an replace. The first key’s the distinctive identifier for an merchandise, and altering the first key’s mainly creating a brand new merchandise. If you wish to change the first key of an present merchandise, you may must delete the previous merchandise and create a brand new one. This two-step course of is slower and dear. Typically you may 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.
Alternatively, in case you have 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 may concern a easy UpdateItem request to alter 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 may have a mutable attribute that you just wish to type on. The canonical examples listed below are a leaderboard for a sport the place individuals are regularly racking up factors, or for a regularly updating record of things the place you wish to show probably the most not too long ago up to date gadgets first. Consider one thing like Google Drive, the place you may type your recordsdata by ‘final modified’.
A second sample the place this comes up is when you may have a mutable attribute that you just wish to filter on. Right here, you may consider an ecommerce retailer with a historical past of orders for a consumer. You might wish to permit the consumer to filter their orders by standing — present me all my orders which might be ‘shipped’ or ‘delivered’. You may construct this into your partition key or the start of your type key to permit exact-match filtering. Because the merchandise adjustments standing, you may replace the standing attribute and lean on DynamoDB to group the gadgets appropriately 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 will lower your expenses by avoiding the additional write prices of the transaction.
Moreover, word that this sample suits properly with the earlier tip. It is unlikely you’ll establish 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. Relatively, you may replace by a extra persistent worth, just like the consumer’s ID, the order ID, or the file’s ID. Then, you may use the secondary index to type 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 is best to intention to unfold requests throughout your partitions to get the advantages of DynamoDB’s scalability.
This usually means it is 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 massive 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 whole desk for a sort of merchandise. In the event that they wish 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 newest 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 purposes the place you will not come near the DynamoDB partition throughput limits, but it surely’s a harmful sample for a high traffic utility. All your 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 to 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 will help you — DynamoDB desires to restrict the staleness of your secondary index, so it would forestall you from a secondary index with a considerable amount of lag. Nonetheless, it may be a stunning state of affairs that pops up whenever you’re least anticipating it.
Use sparse indexes as a worldwide filter
Individuals usually consider secondary indexes as a strategy to replicate all of their information with a brand new main key. Nonetheless, you do not want your whole information to finish up in a secondary index. If in case 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 worldwide filter in your information. The canonical instance I exploit for it is a message inbox. In your essential desk, you may retailer all of the messages for a selected consumer ordered by the point they had been created.
However in case you’re like me, you may have a whole lot of messages in your inbox. Additional, you may deal with unread messages as a ‘todo’ record, like little reminders to get again to somebody. Accordingly, I normally solely wish to see the unread messages in my inbox.
You could possibly use your secondary index to supply this world filter the place unread == true. Maybe your secondary index partition key’s one thing like ${userId}#UNREAD, and the kind key’s the timestamp of the message. While you create the message initially, it would embody the secondary index partition key worth and thus will probably be replicated to the unread messages secondary index. Later, when a consumer 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 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 will save on write prices.
Slender your secondary index projections to cut back index dimension 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 would not have the first key components for the index. This trick can be utilized for not solely main key components but additionally for non-key attributes within the information!
While you create a secondary index, you may specify which attributes from the primary desk you wish to embody within the secondary index. That is referred to as the projection of the index. You may 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 pricey mistake. Keep in mind that each write to your essential desk that adjustments the worth of a projected attribute will probably be replicated to your secondary index. A single secondary index with full projection successfully doubles the write prices to 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 based mostly 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 may be paying the total 4 WCUs on each your essential desk and your secondary index.
Thus, there are two methods that you may lower your expenses by narrowing your secondary index projections. First, you may keep away from sure writes altogether. If in case 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 lowering the scale of the merchandise that’s replicated.
This could be a difficult stability to get proper. Secondary index projections are usually not alterable after the index is created. In the event you discover that you just want extra attributes in your secondary index, you may must create a brand new index with the brand new projection after which delete the previous 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 basic query — must you use a secondary index in any respect?
As we have seen, secondary indexes assist you 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 decreased 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 desirous about different approaches.
To convey this residence, let us take a look at two conditions the place secondary indexes may not make sense.
A number of filterable attributes in small merchandise collections
With DynamoDB, you usually need your main keys to do your filtering for you. It irks me a bit of at any time when 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 wish to over-read your information after which filter in your utility.
The commonest place you may see that is whenever you wish to present a whole lot of totally different filters in your information to your customers, however the related information set is bounded.
Consider a exercise tracker. You may wish to permit customers to filter on a whole lot of attributes, reminiscent of sort of exercise, depth, length, date, and so forth. Nonetheless, the variety of exercises a consumer has goes to be manageable — even an influence consumer will take some time to exceed 1000 exercises. Relatively than placing indexes on all of those attributes, you may simply fetch all of the consumer’s exercises after which filter in your utility.
That is the place I like to recommend doing the mathematics. DynamoDB makes it simple to calculate these two choices and get a way of which one will work higher to your utility.
A number of filterable attributes in massive merchandise collections
Let’s change our state of affairs a bit — what if our merchandise assortment is massive? What if we’re constructing a exercise tracker for a health club, and we wish to permit the health club proprietor to filter on all the attributes we talked about above for all of the customers within the health club?
This adjustments the state of affairs. Now we’re speaking about lots of and even 1000’s of customers, every with lots of or 1000’s of exercises. It will not make sense to over-read the whole 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 may depend on the related filters being current. If we would like our health club proprietor to have the ability to filter on a wide range of attributes, all of that are non-obligatory, we would must create a lot of indexes to make this work.
We talked in regards to the potential 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 may end in a whole lot of forwards and backwards together with your utility, together with some complicated utility logic to determine it out.
When I’ve a lot of these issues, I usually 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 realized 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 right way to use secondary indexes successfully and to study their particular quirks. Lastly, we checked out how to consider secondary indexes to see when it is best to use different approaches.
Secondary indexes are a strong device 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 bounce in.
Be taught extra about how you need to use Rockset for secondary-index-like filtering in Alex DeBrie’s weblog DynamoDB Filtering and Aggregation Queries Utilizing SQL on Rockset.
