Introduction
Indexes are an important 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 in your information.
On this put up, 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 it’s best to use secondary indexes and when it’s best to search for different options.
Let’s get began.
What’s DynamoDB, and what are DynamoDB secondary indexes?
Earlier than we get into use circumstances and finest practices for secondary indexes, we should always first perceive what DynamoDB secondary indexes are. And to do this, we should always perceive a bit about how DynamoDB works.
This assumes some fundamental understanding of DynamoDB. We’ll cowl the essential factors it is advisable to know to grasp secondary indexes, however when you’re new to DynamoDB, chances are you’ll need 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 method, it will possibly 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 large deal — many databases will see lowered efficiency as you enhance the quantity of knowledge or the variety of concurrent requests. Nevertheless, 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 below the hood. These partitions usually are not 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 information lives on. Any request you make will undergo a request router that can decide which partition ought to deal with the request. These partitions are small — usually 10GB or much less — to allow them to be moved, break up, replicated, and in any other case managed independently.
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. Nevertheless, 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’re primarily getting a instantly addressable index in your information.
The API for DynamoDB displays this. There are a sequence of operations on particular person objects (GetItem, PutItem, UpdateItem, DeleteItem) that will let you learn, write, and delete particular person objects. Moreover, there’s a Question operation that permits you to retrieve a number of objects with the identical partition key. In case you have a desk with a composite main key, objects with the identical partition key will probably be grouped collectively on the identical partition. They are going to be ordered based on 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 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 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. The easiest way to grasp the necessity for secondary indexes is to grasp the issue they remedy. We have seen how DynamoDB partitions your information based on 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 is advisable to entry your information otherwise?
In our instance above, we had a desk of customers that we accessed by their group and username. Nevertheless, we can also have to 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 the direction of. As a result of our desk is partitioned by totally different attributes, there’s not a transparent solution to entry our information in the best way we would like. We may do a full desk scan, however that is sluggish and inefficient. We may duplicate our information right into a separate desk with a unique main key, however that provides complexity.
That is the place secondary indexes are available. A secondary index is principally 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 info to your secondary index.
Observe: All the pieces on this part applies to world secondary indexes. DynamoDB additionally offers native secondary indexes, that are a bit totally different. In virtually all circumstances, 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 information, it has simply been reorganized with a unique main key. Now, we will effectively lookup a person by their e mail tackle.
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 totally different in just a few key methods.
First, and most significantly, DynamoDB’s indexes dwell on totally totally different partitions than your important 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 often simply keep the secondary index for all information on the shard. Nevertheless, in case your indexes do not use the shard key, you are shedding a number of the advantages of horizontally scaling your information as a question with out the shard key might want to do a scatter-gather operation throughout all shards to seek out the info you are searching for.
A second method that DynamoDB’s secondary indexes are totally different is that they (typically) copy the whole 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 file within the index, the database will then have to go fetch the total merchandise. As a result of DynamoDB’s secondary indexes are on totally different nodes than the principle desk, they need 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 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 typically double the whole write prices in your desk.
Suggestions for utilizing secondary indexes
Now that we perceive what secondary indexes are and the way they work, let’s discuss methods to use them successfully. Secondary indexes are a strong instrument, 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 it’s 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 principle desk. This leads to further value and additional latency, and you’ll typically keep away from it with some upfront planning.
Should 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 hitch them collectively. In DynamoDB, it’s best to 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 need to design my desk in a method that makes this simple, ideally with out utilizing DynamoDB Transactions or utilizing a read-modify-write sample that may very well be topic to race situations.
As you’re employed via these, you may usually discover that there is a ‘main’ solution to determine 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 straightforward with secondary indexes.
In our Customers instance earlier than, each Person request will doubtless embody the Group and the Username. This may permit me to lookup the person Person file in addition to authorize particular actions by the Person. The e-mail tackle lookup could also be for much less distinguished entry patterns, like a ‘forgot password’ movement or a ‘seek for a person’ movement. 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. Nevertheless, you can’t change the first key of an merchandise in an replace. The first secret’s the distinctive identifier for an merchandise, and altering the first secret’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 outdated merchandise and create a brand new one. This two-step course of is slower and expensive. Typically you may have to learn the unique merchandise first, then use a transaction to delete the unique merchandise and create a brand new one in the identical request.
Then again, 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’ll be able to subject a easy UpdateItem request to alter the worth, and DynamoDB will deal with the remainder.
I see this sample come up in two important conditions. The primary, and most typical, is when you’ve a mutable attribute that you just need to type on. The canonical examples listed below are a leaderboard for a sport the place individuals are frequently racking up factors, or for a frequently updating checklist of things the place you need to show probably the most just lately up to date objects 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’ve a mutable attribute that you just need to filter on. Right here, you may consider an ecommerce retailer with a historical past of orders for a person. Chances are you’ll need to permit the person to filter their orders by standing — present me all my orders which can be ‘shipped’ or ‘delivered’. You’ll be able to construct this into your partition key or the start of your type key to permit exact-match filtering. Because the merchandise modifications standing, you may replace the standing attribute and lean on DynamoDB to group the objects accurately in your secondary index.
In each of those conditions, 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, notice that this sample matches effectively with the earlier tip. It is unlikely you’ll determine an merchandise for writing based mostly on the mutable attribute like their earlier rating, their earlier standing, or the final time they had been up to date. 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 based mostly on the mutable attribute.
Keep away from the ‘fats’ partition
We noticed above that DynamoDB divides your information into partitions based mostly on the first key. DynamoDB goals to maintain these partitions small — 10GB or much less — and it’s best to intention to unfold requests throughout your partitions to get the advantages of DynamoDB’s scalability.
This usually means it’s best to use a high-cardinality worth in your partition key. Consider one thing like a username, an order ID, or a sensor ID. There are giant numbers of values for these attributes, and DynamoDB can unfold the visitors throughout your partitions.
Typically, I see folks perceive this precept of their important desk, however then fully overlook about it of their secondary indexes. Typically, they need ordering throughout the whole desk for a sort 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 type 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 type key.
This sample can work for small-traffic functions the place you will not come near the DynamoDB partition throughput limits, nevertheless it’s a harmful sample for a high traffic software. Your entire 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 could trigger issues in your 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 important desk.
That is designed that can assist you — DynamoDB desires to restrict the staleness of your secondary index, so it can forestall you from a secondary index with a considerable amount of lag. Nevertheless, it may be a shocking 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. Nevertheless, you do not want your whole 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 take advantage of for this can be a message inbox. In your important desk, you may retailer all of the messages for a specific person ordered by the point they had been created.
However when you’re like me, you’ve 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 could possibly use your secondary index to supply this world filter the place unread == true. Maybe your secondary index partition secret’s one thing like ${userId}#UNREAD, and the type secret’s the timestamp of the message. If you create the message initially, it can 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 may change the standing to READ and delete the secondary index partition key worth. DynamoDB will then take away it out of your secondary index.
I take advantage of this trick on a regular basis, and it is remarkably efficient. Additional, a sparse index will prevent cash. Any updates to learn messages won’t be replicated to the secondary index, and you may save on write prices.
Slim your secondary index projections to cut back index measurement and/or writes
For our final tip, let’s take the earlier level just a little 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 information!
If you create a secondary index, you may specify which attributes from the principle desk you need to embody within the secondary index. That is referred to as the projection of the index. You’ll be able to select to incorporate all attributes from the principle desk, solely the first key attributes, or a subset of the attributes.
Whereas it is tempting to incorporate all attributes in your secondary index, this could be a expensive mistake. Keep in mind that each write to your important 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 based mostly on the dimensions of your merchandise. Every 1KB of knowledge written to your desk makes use of a WCU. Should you’re copying a 4KB merchandise to your secondary index, you may be paying the total 4 WCUs on each your 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 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 dimensions of the merchandise that’s replicated.
This could be a tough steadiness to get proper. Secondary index projections usually are not alterable after the index is created. Should you discover that you just 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 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 provide help to entry your information otherwise. Nevertheless, 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 residence, let’s take a look at two conditions the place secondary indexes may not make sense.
A lot of filterable attributes in small merchandise collections
With DynamoDB, you usually need your main keys to do your filtering for you. It irks me just a little 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 may need to over-read your information after which filter in your software.
The most typical place you may see that is whenever you need to present loads of totally different filters in your information in 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 kind of exercise, depth, period, date, and so forth. Nevertheless, 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 software.
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 software.
A lot of filterable attributes in giant merchandise collections
Let’s change our scenario a bit — what if our merchandise assortment is giant? What if we’re constructing a exercise tracker for a health club, and we need 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 modifications the scenario. Now we’re speaking about tons of and even hundreds of customers, every with tons of or hundreds of exercises. It will not make sense to over-read the 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 recognized 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 quite a lot of attributes, all of that are non-compulsory, we might have to create numerous indexes to make this work.
We talked concerning the potential downsides of question planners earlier than, however question planners have an upside too. Along with permitting for extra versatile queries, they will additionally do issues like index intersections to have a look at partial outcomes from a number of indexes in composing these queries. You are able to do the identical factor with DynamoDB, however it should lead to loads of forwards and backwards along with your software, together with some complicated software logic to determine it out.
When I’ve a lot of these issues, I usually search for a instrument 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 put up, 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 methods 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 you rigorously contemplate your entry patterns and depend the prices earlier than you soar in.
Be taught extra about how you should use Rockset for secondary-index-like filtering in Alex DeBrie’s weblog DynamoDB Filtering and Aggregation Queries Utilizing SQL on Rockset.