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 robust device for enabling new entry patterns in your information.
On this submit, 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 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 finest practices for secondary indexes, we should always first perceive what DynamoDB secondary indexes are. And to try this, we should always perceive a bit about how DynamoDB works.
This assumes some fundamental understanding of DynamoDB. We’ll cowl the fundamental factors you’ll want to know to grasp secondary indexes, however should you’re new to DynamoDB, it’s possible you’ll wish to begin with a extra fundamental introduction.
The Naked Minimal you Must 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 procuring cart, liking a video, or including a touch upon Reddit. In that approach, it might deal with comparable functions 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 knowledge or 1 petabyte of knowledge, DynamoDB desires to have the identical latency in your OLTP-like requests. This can be a massive deal — many databases will see lowered efficiency as you enhance the quantity of knowledge or the variety of concurrent requests. Nonetheless, offering these ensures requires some tradeoffs, and DynamoDB has some distinctive traits that you’ll want 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 in your desk (both a single factor, known as a ‘partition key’, or a mix of a partition key and a kind 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 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, break up, replicated, and in any other case managed independently.
Horizontal scalability by way of sharding is fascinating however is on no account 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. Reasonably 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 might be basically getting a straight addressable index in your information.
The API for DynamoDB displays this. There are a collection of operations on particular person objects (GetItem, PutItem, UpdateItem, DeleteItem) that assist you to 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’ve got a desk with a composite major key, objects with the identical partition key will probably be grouped collectively on the identical partition. They are going to be ordered in line with the type 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 System”.
For instance, we could say a SaaS software 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 kind key of ‘Username’. This permits 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 take a look at secondary indexes. One of the simplest 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 line with your major key and the way it pushes you to make use of the first key to entry your information. That is all effectively and good for some entry patterns, however what if you’ll want to entry your information another way?
In our instance above, we had a desk of customers that we accessed by their group and username. Nonetheless, we may have to fetch a single person by their e mail handle. 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 solution to entry our information in the way in which we wish. We might do a full desk scan, however that is sluggish and inefficient. We might duplicate our information right into a separate desk with a distinct 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 distinct 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 info 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 virtually all instances, you will have a worldwide secondary index. For extra particulars on the variations, take a look at 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 “E-mail”. The secondary index will look as follows:
Discover that this is identical information, it has simply been reorganized with a distinct 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 specific attribute. However DynamoDB’s secondary indexes are completely different in a couple of key methods.
First, and most significantly, DynamoDB’s indexes stay on totally completely different partitions than your fundamental 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 information by the attributes you will use to question it.
In different distributed databases, they often do not reshard your information for the secondary index. They will normally simply preserve the secondary index for all information on the shard. Nonetheless, in case your indexes do not use the shard key, you are shedding a few 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 search out the info you are in search of.
A second approach that DynamoDB’s secondary indexes are completely different is that they (typically) copy your entire merchandise to the secondary index. For indexes on a relational database, the index will typically comprise a pointer to the first key of the merchandise being listed. After finding a related document within the index, the database will then have to go fetch the complete merchandise. As a result of DynamoDB’s secondary indexes are on completely different nodes than the primary desk, they wish to keep away from a community hop again to the unique merchandise. As a substitute, you will 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’ll be able to’t write on to a secondary index. Reasonably, you’ll write to your fundamental 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 typically double the overall 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 discuss how you can use them successfully. Secondary indexes are a robust device, however they are often misused. Listed below 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 you must goal 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 ends in further value and further latency, and you’ll typically keep away from it with some upfront planning.
In the event you’ve learn something about DynamoDB information 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 hitch them collectively. In DynamoDB, you must take into consideration the actions your software 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 typically sustaining some kind of constraint — uniqueness on a username or a most variety of members in a bunch. I wish to design my desk in a approach that makes this simple, 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 via these, you will usually discover that there is a ‘major’ solution to establish your merchandise that matches up along with your write patterns. This may find yourself being your major key. Then, including in extra, secondary learn patterns is simple with secondary indexes.
In our Customers instance earlier than, each Person request will probably embody the Group and the Username. This may enable me to lookup the person Person document 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’ circulation or a ‘seek for a person’ circulation. These are read-only patterns, they usually match effectively 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 means that you can replace an current 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 current merchandise, you will have to delete the outdated merchandise and create a brand new one. This two-step course of is slower and dear. Typically you will 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.
However, when you have 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 situation a easy UpdateItem request to alter the worth, and DynamoDB will deal with the remainder.
I see this sample come up in two fundamental conditions. The primary, and most typical, is when you could have a mutable attribute that you simply wish to type on. The canonical examples listed below are a leaderboard for a sport the place persons are frequently racking up factors, or for a frequently updating checklist of things the place you wish to show probably the most lately up to date objects first. Consider one thing like Google Drive, the place you’ll be able to type your recordsdata by ‘final modified’.
A second sample the place this comes up is when you could have a mutable attribute that you simply wish to filter on. Right here, you’ll be able to consider an ecommerce retailer with a historical past of orders for a person. It’s possible you’ll wish 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 modifications 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 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 matches effectively 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 have been up to date. Reasonably, 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 information into partitions primarily based on the first key. DynamoDB goals to maintain these partitions small — 10GB or much less — and you must goal to unfold requests throughout your partitions to get the advantages of DynamoDB’s scalability.
This usually 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 fundamental desk, however then fully 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 wish 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 latest 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, however it’s a harmful sample for a high traffic software. Your entire site visitors could also be funneled to a single bodily partition, and you’ll shortly hit the write throughput limits for that partition.
Additional, and most dangerously, this may trigger issues in your fundamental 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 fundamental desk.
That is designed that will help 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 state of affairs that pops up whenever you’re least anticipating it.
Use sparse indexes as a worldwide filter
Folks typically consider secondary indexes as a solution 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. When you’ve got 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 take advantage of for this can be a message inbox. In your fundamental desk, you would possibly retailer all of the messages for a specific person ordered by the point they have been created.
However should you’re like me, you could have plenty of messages in your inbox. Additional, you would possibly deal with unread messages as a ‘todo’ checklist, like little reminders to get again to somebody. Accordingly, I normally solely wish to see the unread messages in my inbox.
You would use your secondary index to offer this world filter the place unread == true. Maybe your secondary index partition key’s one thing like ${userId}#UNREAD, and the type key’s the timestamp of the message. If you create the message initially, it should embody the secondary index partition key worth and thus will probably 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 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 won’t be replicated to the secondary index, and you will 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 little bit additional. We simply noticed that DynamoDB will not embody an merchandise in your secondary index if the merchandise does not have the first key components for the index. This trick can be utilized for not solely major key components but in addition for non-key attributes within the information!
If you create a secondary index, you’ll be able to specify which attributes from the primary desk you wish to embody within the secondary index. That is known 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 pricey mistake. Do not forget that each write to your fundamental desk that modifications 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 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 dimensions of your merchandise. Every 1KB of knowledge 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 fundamental desk and your secondary index.
Thus, there are two methods which you can lower your expenses by narrowing your secondary index projections. First, you’ll be able to keep away from sure writes altogether. When you’ve got 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 dimensions of the merchandise that’s replicated.
This could be a difficult steadiness to get proper. Secondary index projections aren’t alterable after the index is created. In the event you discover that you simply want extra attributes in your secondary index, you will have to create a brand new index with the brand new projection after which delete the outdated 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 another way. 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 enthusiastic about different approaches.
To carry this dwelling, let us take a look at two conditions the place secondary indexes won’t make sense.
Plenty 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 software — 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 wish to over-read your information after which filter in your software.
The most typical place you will see that is whenever you wish to present plenty of completely different filters in your information in your customers, however the related information set is bounded.
Consider a exercise tracker. You would possibly wish to enable customers to filter on plenty of attributes, similar to kind of exercise, depth, period, 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. Reasonably 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 software.
That is the place I like to recommend doing the maths. DynamoDB makes it simple to calculate these two choices and get a way of which one will work higher in your software.
Plenty 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 gymnasium, and we wish 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 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 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 recognized entry patterns the place you’ll be able to 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 non-obligatory, we might have to create a lot of 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 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 plenty of backwards and forwards along with your software, together with some complicated software logic to determine it out.
When I’ve some of these issues, I usually search for a device 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.
Conclusion
On this submit, 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 how you can 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 information modeling, be sure you rigorously think about your entry patterns and depend the prices earlier than you soar 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.
