Introduction
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 instrument for enabling new entry patterns on your information.
On this submit, 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 clear up. Then, we’ll have 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 instances and greatest 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 primary understanding of DynamoDB. We’ll cowl the fundamental factors it’s good to know to know secondary indexes, however in the event you’re new to DynamoDB, you could need to begin with a extra primary introduction.
The Naked Minimal you Must Find out 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 approach, it could actually 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 knowledge or 1 petabyte of knowledge, DynamoDB needs to have the identical latency on your OLTP-like requests. It is a huge 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 it’s good to perceive to make use of it successfully.
First, DynamoDB horizontally scales your databases by spreading your information throughout a number of partitions underneath 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 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 main 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 — typically 10GB or much less — to allow them to be moved, break up, replicated, and in any other case managed independently.
Horizontal scalability through sharding is fascinating 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 information. Relatively than utilizing a question planner that interprets your requests right into a collection of queries, DynamoDB forces you to make use of your main key to entry your information. You might be primarily getting a straight addressable index on your information.
The API for DynamoDB displays this. There are a collection of operations on particular person gadgets (GetItem, PutItem, UpdateItem, DeleteItem) that permit you to learn, write, and delete particular person gadgets. Moreover, there’s a Question operation that permits you to retrieve a number of gadgets with the identical partition key. In case you have a desk with a composite main key, gadgets with the identical partition key can 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 Consumer” or “Fetch the final 10 Sensor Readings for an IoT System”.
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 form key of ‘Username’. This permits us to do operations to fetch or replace a person Consumer by offering their Group and Username. We will additionally fetch the entire 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 know the necessity for secondary indexes is to know the issue they clear up. We have seen how DynamoDB partitions your information in keeping with your main 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 it’s good to entry your information otherwise?
In our instance above, we had a desk of customers that we accessed by their group and username. Nonetheless, we may additionally have to fetch a single person by their electronic 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 solution to entry our information in the best way we wish. 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 special main key, however that provides complexity.
That is the place secondary indexes are available. A secondary index is principally a completely managed copy of your information with a special 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.
Be aware: All the pieces 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 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 “Electronic mail”. The secondary index will look as follows:
Discover that this is similar information, it has simply been reorganized with a special main key. Now, we will effectively search for a person by their electronic mail handle.
In some methods, that is similar to an index in different databases. Each present a knowledge construction that’s optimized for lookups on a specific attribute. However DynamoDB’s secondary indexes are completely different in just a few key methods.
First, and most significantly, DynamoDB’s indexes dwell on fully completely different partitions than your major desk. DynamoDB needs each lookup to be environment friendly and predictable, and it needs to offer 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 often do not reshard your information for the secondary index. They’re going to often simply preserve the secondary index for all information on the shard. Nonetheless, in case your indexes do not use the shard key, you are dropping 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 information you are searching for.
A second approach that DynamoDB’s secondary indexes are completely different is that they (typically) copy your complete merchandise to the secondary index. For indexes on a relational database, the index will typically include a pointer to the first key of the merchandise being listed. After finding a related file 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 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. Relatively, you’ll write to your major 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 on your desk.
Ideas for utilizing secondary indexes
Now that we perceive what secondary indexes are and the way they work, let’s discuss the right way to use them successfully. Secondary indexes are a strong instrument, 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 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 principle desk. This leads to additional price and further latency, and you may 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 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 start out with the write-based entry patterns first. With my writes, I am typically sustaining some sort of constraint — uniqueness on a username or a most variety of members in a gaggle. I need 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 might be topic to race situations.
As you’re employed via these, you may typically discover that there is a ‘main’ solution to establish your merchandise that matches up along with your write patterns. This may find yourself being your main key. Then, including in extra, secondary learn patterns is simple with secondary indexes.
In our Customers instance earlier than, each Consumer request will seemingly embrace the Group and the Username. This may permit me to search for the person Consumer file in addition to authorize particular actions by the Consumer. 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 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 have a look at conditions the place it applies.
DynamoDB permits you to 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 principally creating a brand new merchandise. If you wish to change the first key of an present merchandise, you may have to delete the previous merchandise and create a brand new one. This two-step course of is slower and dear. 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.
Alternatively, you probably 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 possibly can difficulty a easy UpdateItem request to vary the worth, and DynamoDB will deal with the remainder.
I see this sample come up in two major conditions. The primary, and most typical, is when you’ve got a mutable attribute that you simply need 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 need to show essentially the most lately up to date gadgets first. Consider one thing like Google Drive, the place you may type your information by ‘final modified’.
A second sample the place this comes up is when you’ve got a mutable attribute that you simply 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 permit the person to filter their orders by standing — present me all my orders which can 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 may replace the standing attribute and lean on DynamoDB to group the gadgets 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 will get monetary savings 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 had been up to date. Relatively, 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 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 it’s best to goal to unfold requests throughout your partitions to get the advantages of DynamoDB’s scalability.
This typically 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 site visitors throughout your partitions.
Typically, I see individuals perceive this precept of their major desk, however then utterly overlook about it of their secondary indexes. Typically, they need ordering throughout your 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, however it’s a harmful sample for a heavy-traffic utility. Your entire site 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 major 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 major desk.
That is designed that will help you — DynamoDB needs to restrict the staleness of your secondary index, so it’s going to stop you from a secondary index with a considerable amount of lag. Nonetheless, it may be a stunning scenario that pops up whenever you’re least anticipating it.
Use sparse indexes as a world filter
Folks typically consider secondary indexes as a solution to replicate all of their information with a brand new main key. Nonetheless, you do not want your entire information to finish up in a secondary index. 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 world filter in your information. The canonical instance I exploit for this can be a message inbox. In your major desk, you may retailer all of the messages for a specific person ordered by the point they had been created.
However in the event you’re like me, you’ve got loads 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 often solely need to see the unread messages in my inbox.
You possibly can 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. While you create the message initially, it’s going to embrace 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 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 is not going to be replicated to the secondary index, and you will 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 slightly 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 main key parts but in addition for non-key attributes within the information!
While 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 generally is a pricey mistake. Keep in mind that each write to your major 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 on 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 knowledge 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 complete 4 WCUs on each your major desk and your secondary index.
Thus, there are two methods that you could get monetary savings by narrowing your secondary index projections. First, you may keep away from sure writes altogether. 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 decreasing the scale of the merchandise that’s replicated.
This generally is a difficult 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 may have to 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 otherwise. 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 excited about different approaches.
To deliver this residence, 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 slightly each 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 information after which filter in your utility.
The most typical place you may see that is whenever you need to present loads of completely different filters in your information on your customers, however the related information set is bounded.
Consider a exercise tracker. You may need to permit customers to filter on loads of attributes, corresponding to sort 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 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 maths. DynamoDB makes it straightforward to calculate these two choices and get a way of which one will work higher on your utility.
Numerous 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 health club, and we need to permit the health club proprietor to filter on the entire attributes we talked about above for all of the customers within the health club?
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 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 rely on the related filters being current. If we wish our health club proprietor to have the ability to filter on quite a lot of attributes, all of that are non-obligatory, we would have to create numerous 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 may end in loads of backwards and forwards along with your utility, together with some complicated utility logic to determine it out.
When I’ve a lot of these issues, I typically search for a instrument 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 submit, we discovered about DynamoDB secondary indexes. First, we checked out some conceptual bits to know 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 be taught 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 instrument in your DynamoDB toolbox, however they don’t seem to be a silver bullet. As with all DynamoDB information modeling, be sure to fastidiously think about your entry patterns and rely the prices earlier than you bounce in.
Study 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.