dynamodb hot partition problem solution

Think twice when designing your data structure and especially when defining the partition key: Guidelines for Working with Tables. Due to the table size alone, we estimate having grown from around 16 to 64 partitions (note that determining this is not an exact science). We’re also up over 400% on test runs since the original migration. The main issue is that using a naive partition key/range key schema will typically face the hot key/partition problem, or size limitations for the partition, or make it impossible to play events back in sequence. Click to share on Twitter (Opens in new window), Click to share on LinkedIn (Opens in new window), Click to share on Reddit (Opens in new window), Click to share on WhatsApp (Opens in new window), Click to share on Skype (Opens in new window), Click to share on Facebook (Opens in new window), Click to email this to a friend (Opens in new window), Using DynamoDB in Production – New Course, DynamoDB: Monitoring Capacity and Throttling, Pluralsight Course: Getting Started with DynamoDB, Partition Throttling: How to detect hot Partitions / Keys. Sorry, your blog cannot share posts by email. Don’t worry too much about being strict about uniform access, I’ve rarely seem perfectly distributed data in a table, you just need it distributed enough. Initial testing seems great, but we have seem to hit a point where scaling the write throughput up doesn't scale out of throttles. For this table, test_id and result_id were chosen as the partition key and range key respectively. We will also illustrate common techniques you can use to avoid the “hot” partition problem that’s often associated with partitioning tenant data in a pooled model. Hot Partitions. TESTING AGAINST A HOT PARTITION To explore this ‘hot partition’ issue in greater detail, we ran a single YCSB benchmark against a single partition on a 110MB dataset with 100K partitions. HBase gives you a console to see how these keys are spread over the various regions so you can tell where your hot spots are. DynamoDB read/write capacity modes Then check if the sum is divisible by 3 or not, if it is not divisible then we can partition the set in 3 parts. To avoid hot partition, you should not use the same partition key for a lot of data and access the same key too many times. You can do this by hooking into the AWS SDK, on retries or errors. You should evaluate various approaches based on your data ingestion and access pattern, then choose the most appropriate key with the least probability of hitting throttling issues. Naive solutions: The throughput capacity allocated to each partition, 3. In order to achieve this, there must be a mechanism in place that dynamically partitions the entire data over a set of storage nodes. Retrieve a single image by its URL path (READ); 3. It didn’t take long for scaling issues to arise as usage grew heavily, with many tests being run on a by-the-minute schedule generating millions of test runs. Take, for instance, a “Login & Checkout” test which makes a few HTTP calls and verifies the response content and status code of each. Pool Model A silo model often represents the simplest path forward if you have compliance or other isolation needs and want to avoid noisy neighbor conditions. The partition key portion of a table's primary key determines the logical partitions in which a table's data is stored. All items with the same partition key are stored together, in sorted order by sort key value. It Hasn’t Been 2% for 30 Years (Here’s Proof). It still exists. Learn about what partitions are, the limits of a partition, when and how partitions are created, the partitioning behavior of DynamoDB, and the hot key problem. While the format above could work for a simple table with low write traffic, we would run into an issue at higher load. When storing data, Amazon DynamoDB divides a table into multiple partitions and distributes the data based on the hash key element of the primary key. From the DynamoDB documentation: To achieve the full amount of request throughput you have provisioned for a table, keep your workload spread evenly across the partition key values. A good understanding of how partitioning works is probably the single most important thing in being successful with DynamoDB and is necessary to avoid the dreaded hot partition problem. The problem is the distribution of throughput across nodes. This Amazon blog post is a much recommended read to understand the importance of selecting the right partition key and the problem of hot keys. hide. Before you would be wary of hot partitions, but I remember hearing that partitions are no longer an issue or is that for s3? Partition problem is special case of Subset Sum Problem which itself is a special case of the Knapsack Problem.The idea is to calculate sum of all elements in the set. Currently focusing on helping SaaS products leverage technology to innovate, scale and be market leaders. The "split" also appears to be persistent over time. This is especially significant in pooled multi-tenant environments where the use of a tenant identifier as a partition key could concentrate data in a given partition. The problem with storing time based events in DynamoDB, in fact, is not trivial. So the number of writes each run, within a small timeframe, is: Shortly after our migration to DynamoDB, we released a new feature named Test Environments. As far as I know there is no other solutions of comparable scale / maturity out there. We make a database GET request given userId as the partition key and the contact as the sort key to check the block existence. Investigating DynamoDB latency. DynamoDB: Read Path on the Sample Table. Avoid hot partition. In 2018, AWS introduced adaptive capacity, which reduced the problem, but it still very much exists. Every time an API test is run, we store the results of those tests in a database. Over-provisioning to handle hot partitions. In DynamoDB, the total provisioned IOPS is evenly divided across all the partitions. While Amazon has managed to mitigate this to some extent with adaptive capacity, the problem is still very much something you need to design your data layout to avoid. As you design, develop, and build software-as-a-service (SaaS) solutions on Amazon Web Services (AWS), you must think about how you want to partition the data that belongs to each of your customers, which are commonly referred to as tenants … To get the most out of DynamoDB read and write request should be distributed among different partition keys. 3 cost-cutting tips for Amazon DynamoDB How to avoid costly mistakes with DynamoDB partition keys, read/write capacity modes, and global secondary indexes A lot. When we first launched API tests at Runscope two years ago, we stored the results of these tests in a PostgreSQL database that we managed on EC2. Check it out. DynamoDB has a few different modes to pick from when provisioning RCUs and WCUs for your tables. Also, there are reasons to believe that the split works in response to a high usage of throughput capacity on a single partition, and that it always happens by adding a single node, so that the capacity is increased by 1kWCUs / 3k RCUs each time. If you have billions of items, with say 1000 internal partitions, each partition can only serve up to 1/1000 throughput of your total table capacity. You don’t need to worry about accessing some partition keys more than other keys in terms of throttling or cost. The problem arises because capacity is evenly divided across partitions. First, some quick background: a Runscope API test can be scheduled to run up to once per minute and we do a small fixed number of writes for each. Customers can then review the logs and debug API problems or share results with other team members or stakeholders. It looks like DynamoDB, in fact, has a working auto-split feature for hot partitions. The php sdk adds a PHPSESSID_ string to the beginning of the session id. E.g if top 0.01% of items which are mostly frequently accessed are happen to be located in one partition, you will be throttled. All the storages impose some limit on item size or attribute size. Partner Solutions Architect, AWS SaaS Factory By Tod Golding, Principal Partner Solutions Architect, AWS SaaS Factory. To avoid hot partition, you should not use the same partition key for a lot of data and access the same key too many times. We were steadily doing 300 writes/second but needed to provision for 2,000 in order to give a few hot partitions just 25 extra writes/second — and we still saw throttling. As per the Wikipedia page, “Consistent hashing is a special kind of hashing such that when a hash table is resized and consistent hashing is used, only K/n keys need to be remapped on average, where K is the number of keys, and n… The output from the hash function determines the partition in which the item will be stored. Increase the view count on an image (UPDATE); 4. What is a hot key? Thus, with one active user and a badly designed schema for your table, you have a “hot partition” at hand, but DynamoDB is optimized for uniform distribution of items across partitions. DynamoDB uses the partition key value as input to an internal hash function. database. The first three a… Try Dynobase to accelerate DynamoDB workflows with code generation, data exploration, bookmarks and more. Also, there are reasons to believe that the split works in response to a high usage of throughput capacity on a single partition, and that it always happens by adding a single node, so that the capacity is increased by 1kWCUs / 3k RCUs each time. Every time a run of this test is triggered, we store data about the overall result — the status, timestamp, pass/fail, etc. This is commonly referred to as the “hot partition” problem and resulted in us getting throttled. I it possible now to have lets say 30 partition keys holding 1TB of data with 10k WCU & RCU? DynamoDB is great, but partitioning and searching are hard; We built alternator and migration-service to make life easier; We open sourced a sidecar to index DynamoDB tables in Elasticsearch that you should totes use. DynamoDB splits its data across multiple nodes using consistent hashing. Besides, we weren’t having any issues initially, so no big deal right? When storing data, Amazon DynamoDB divides a table into multiple partitions and distributes the data based on the partition key element of the primary key. To accommodate uneven data access patterns, DynamoDB adaptive capacity lets your application continue reading and writing to hot partitions without request failures (as long as you don’t exceed your overall table-level throughput, of course). We initially thought this was a hot partition problem. A hot partition is a partition that receives more requests (write or read) than the rest of the partitions. It causes an intensified load on one of the partitions, while others are accessed much less often. 13 comments. DynamoDB works by allocating throughput to nodes. Balanced writes — a solution to the hot partition problem. report. Adaptive capacity works by automatically increasing throughput capacity for partitions that receive more traffic. In DynamoDB, the total provisioned IOPS is evenly divided across all the partitions. Effects of the "hot partition" problem in DynamoDB. If you have any questions about what you’ve read so far, feel free to ask in the comments section below and I’m happy to answer them. As you design, develop, and build SaaS solutions on AWS, you must think about how you want to partition the data that belongs to each of your customers (tenants). The problem with storing time based events in DynamoDB, in fact, is not trivial. Solution. In Part 2 of our journey migrating to DynamoDB, we’ll talk about how we actually changed the partition key (hint: it involves another migration) and our experiences with, and the limitations of, Global Secondary Indexes. Fundamentally, the problem seems to be that choosing a partitioning key that's appropriate for DynamoDB's operational properties is ... unlikely. Why NoSQL? This kind of imbalanced workload can lead to hot partitions and in consequence - throttling.Adaptive Capacity aims to solve this problem bt allowing to continue reading and writing form these partitions without rejections. Retrieve the top N images based on total view count (LEADERBOARD). Over-provisioning capacity units to handle hot partitions, i.e., partitions that have disproportionately large amounts of data than other partitions. Over-provisioning capacity units to handle hot partitions, i.e., partitions that have disproportionately large amounts of data than other partitions. Below is a snippet of code to demonstrate how to hook into the SDK. Essentially, what this means is that when designing your NoS Some of their main problems were . DynamoDB uses the partition key as an input to an internal hash function in which the result determines which partition the item will be stored in. Investigating DynamoDB latency. Keep in mind, an error means the request is returned to your application, where as a retry means the SDK is going to retry again. We realized that our partition key wasn’t perfect for maximizing throughput but it gave us some indexing for free. Based on this, we have four main access patterns: 1. AWS Specialist, passionate about DynamoDB and the Serverless movement. The output from the hash function determines the partition in which the item will be stored. Transparent support for data compression. Being a distributed database (made up of partitions), DynamoDB under the covers, evenly distributes its provisioned throughput capacity, evenly across all partitions. Otherwise, a hot partition will limit the maximum utilization rate of your DynamoDB table. We rely on several AWS products to achieve this and we recently finished a large migration over to DynamoDB. S 1 = {3,1,1} S 2 = {2,2,1}. This thread is archived. There is no sharing of provisioned throughput across partitions. Primary Key Design In one of my recent projects, there was a requiremen t of writing 4 million records in DynamoDB within 22 minutes. As highlighted in The million dollar engineering problem, DynamoDB’s pricing model can easily make it the single most expensive AWS service for a fast growing company. If no sort key is used, no two items can have the same partition key value. Hot partition occurs when you have a lot of requests that are targeted to only one partition. DynamoDB automatically creates Partitions for: Every 10 GB of Data or When you exceed RCUs (3000) or WCUs (1000) limits for a single partition When DynamoDB sees a pattern of a hot partition, it will split that partition in an attempt to fix the issue. Getting this wrong could mean restructuring data, redesigning APIs, full table migrations or worse at some time in the future when the system has hit a critical threshold. While allocating capacity resources, Amazon DynamoDB assumes a relatively random access pattern across all primary keys. Basic rule of thumb is to distribute the data among different partitions to achieve desired throughput and avoid hot partitions that will limit the utilization of your DynamoDB table to it’s maximum capacity. One might say, “That’s easily fixed, just increase the write throughput!” The fact that we can do this quickly is one of the big upshots of using DynamoDB, and it’s something that we did use liberally to get us out of a jam. This in turn affects the underlying physical partitions. DynamoDB Keys Best Practices. Best practice for DynamoDB recommends that we do our best to have uniform access patterns across items within a table, in turn, evenly distributed the load across the partitions. The AWS SDK has some nice hooks to enable you to know when the request you’ve performed is retrying or has received an error. Let's understand why, and then understand how to handle it. We recently went over how we made a sizable migration to DynamoDB , encountering the “hot partition” problem that taught us the importance of understanding partitions when des Silo vs. Amazon DynamoDB stores data in partitions. Today we have about 400GB of data in this table (excluding indexes), which continues to grow rapidly. This post originally appeared on the Runscope blog and is the first in a two-part series by Runscope Engineer Garrett Heel (see Part 2). Provisioned I/O capacity for the table is divided evenly among these physical partitions. We are experimenting with moving our php session data from redis to DynamoDB. Conceptually this is how we can solve this. Cost Issues — Nike’s Engineering team has written about cost issues they faced with DynamoDB with a couple of solutions too. With on-demand mode, you only pay for successful read and write requests. Are DynamoDB hot partitions a thing of the past? Sometimes your read and writes operations are not evenly distributed among keys and partitions. Check it out. Part 2: Correcting Partition Keys. S 1 = {1,1,1,2} S 2 = {2,3}.. As highlighted in The million dollar engineering problem, DynamoDB’s pricing model can easily make it the single most expensive AWS service for a fast growing company. Which partition each item is allocated to. Since DynamoDB will arbitrary limit each partition to the total throughput divided by number of … As mentioned earlier, the key design requirement for DynamoDB is to scale incrementally. Still using AWS DynamoDB Console? DynamoDB uses the partition key value as input to an internal hash function. Although this cause is somewhat alleviated by adaptive capacity, it is still best to design DynamoDB tables with sufficiently random partition keys to avoid this issue of hot partitions and hot keys. All items with the same partition key are stored together, in sorted order by sort key value. This post is the second in a two-part series about migrating to DynamoDB by Runscope Engineer Garrett Heel (see Part 1). Problem. Additionally, these can be configured to run from up to 12 locations simultaneously. By Anubhav Sharma, Sr. The solution was to increase the number of splits using the `dynamodb.splits` This allows DynamoDB to split the entire table data into smaller partitions, based on the Partition Key. This is not a long term solution and quickly becomes very expensive. Chapter 3: Consistency, DynamoDB streams, TTL, Global tables, DAX, Object-Oriented Programming is The Biggest Mistake of Computer Science, Now Is the Perfect Time to Ruin Donald Trump’s Life. DynamoDB Pitfall: Limited Throughput Due to Hot Partitions In this post we examine how to correct a common problem with DynamoDB involving throttled and … It looks like DynamoDB, in fact, has a working auto-split feature for hot partitions. To add to the complexity, the AWS SDKs tries its best to handle transient errors for you. So, the table shown above will be split into partitions like shown below, if Hotel_ID is . DynamoDB: Partition Throttling How to detect hot Partitions / Keys Partition Throttling: How to detect hot Partitions / Keys. DynamoDB hot partition? Our primary key is the session id, but they all begin with the same string. Although this cause is somewhat alleviated by adaptive capacity, it is still best to design DynamoDB tables with sufficiently random partition keys to avoid this issue of hot partitions and hot keys. DynamoDB will try to evenly split the RCUs and WCUs across Partitions. Here I’m talking about solutions I’m familiar with: AWS DynamoDB, MS Azure Storage Tables, Google AppEngine Datastore. When it comes to DynamoDB partition key strategies, no single solution fits all use cases. This made it much easier to run a test with different/reusable sets of configuration (i.e local/test/production). Each write for a test run is guaranteed to go to the same partition, due to our partition key, The number of partitions has increased significantly, Some tests are run far more frequently than others. The main issue is that using a naive partition key/range key schema will typically face the hot key/partition problem, or size limitations for the partition, or make it impossible to play events back in sequence. The test exposed a DynamoDB limitation when a specific partition key exceeded 3000 read capacity units (RCU) and/ or 1000 write capacity units (WCU). The partition key portion of a table's primary key determines the logical partitions in which a table's data is stored. This had a great response in that customers were condensing their tests and running more now that they were easier to configure. One might say, “That’s easily fixed, just increase the write throughput!” The fact that we can do this quickly is one of the big upshots of using DynamoDB, and it’s something that we did use liberally to get us out of a jam. This is especially significant in pooled multi-tenant environments where the use of a tenant identifier as a partition key could concentrate data in a given partition. Avoid hot partition. The php sdk adds a PHPSESSID_ string to the beginning of the session id. Otherwise, we check if 3 subsets with sum equal to sum/ 3 exists or not in the set. Our equation grew to. Depending on traffic you may want to check DAX to mitigate the hot partition problem – FelixEnescu Feb 11 '18 at 16:29 @blueCat Yeah I have looked at that, looks very promising but unfortunately not available in all regions yet and is a little too expensive compared to elasticache. DynamoDB employs consistent hashing for this purpose. One of the solutions to avoid hot-keys was using Amazon DynamoDB Accelerator ( DAX ), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement, even at millions of requests per second. This means that you can run into issues with ‘hot’ partitions, where particular keys are used much more than others. In this post, experts from AWS SaaS Factory focus on what it means to implement the pooled model with Amazon DynamoDB. We needed a randomizing strategy for the partition keys, to get a more uniform distribution of items across DynamoDB partitions. In short, partitioning the data in a sub-optimal manner is one cause of increasing costs with DynamoDB. This makes it very difficult to predict throttling caused by an individual “hot partition”. Naïve solution: 3-partition problem. Over-provisioning to handle hot partitions. Thus, with one active user and a badly designed schema for your table, you have a “hot partition” at hand, but DynamoDB is optimized for uniform distribution of items across partitions. We considered a few alternatives, such as HBase, but ended up choosing DynamoDB since it was a good fit for the workload and we’d already had some operational experience with it. × × , Using DynamoDB on your local with NoSQL Workbench, Amazon DynamoDB Deep Dive. This Amazon blog post is a much recommended read to understand the importance of selecting the right partition key and the problem of hot keys. The throughput is set up as follows: Each write capacity unit gives 1KB/s of write throughput; Each read capacity unit gives 4KB/s of read throughput; This seems simple enough but an issue arises in how dynamo decides to distribute the requested capacity. TESTING AGAINST A HOT PARTITION To explore this ‘hot partition’ issue in greater detail, we ran a single YCSB benchmark against a single partition on a 110MB dataset with 100K partitions. Naive solutions: First, sum up all the elements of the set. It is possible to have our requests throttled, even if the table’s provisioned capacity / consumed capacity appears healthy like this: This has stumped many users of DynamoDB, so let me explain. In 2018, AWS introduced adaptive capacity, which reduced the problem, but it still very much exists. While Amazon has managed to mitigate this to some extent with adaptive capacity, the problem is still very much something you need to design your data layout to avoid. share. A simple way to solve this problem would be to limit API calls but to keep our service truly scalable, we decided to improve the write sharding. I found this to be very useful, and a must have in the general plumbing for any application using DynamoDB. At Runscope, an API performance monitoring and testing company, we have a small but mighty DevOps team of three, so we’re constantly looking at better ways to manage and support our ever growing infrastructure requirements. In this example, we're a photo sharing website. Partitions, partitions, partitions. Our customers use Runscope to run a wide variety of API tests: on local dev environments, private APIs, public APIs and third-party APIs from all over the world. Unfortunately, DynamoDB does not enable us to see: 2. If you recall, the block service is invoked on — and adds overhead to — every call or SMS, in and out. The principle behind a hot partition is that the representation of your data causes a given partition to receive a higher volume of read or write traffic (compared to other partitions). It's an item with the key that is accessed much more frequently than the rest of the items. The "split" also appears to be persistent over time. Analyse the DynamoDB table data structure carefully when designing your solution and especially when creating a Global Secondary Index and selecting the partition key. The initial migration to DynamoDB involved a few tables, but we’ll focus on one in particular which holds test results. Here are the top 6 reasons why DynamoDB costs spiral out of control. Hot Partitions and Write-Sharding. A good understanding of how partitioning works is probably the single most important thing in being successful with DynamoDB and is necessary to avoid the dreaded hot partition problem. Table at the expense of a little extra index work deal right market leaders index and selecting partition... Made it much easier to configure to talk about in an upcoming write up let 's why! On test runs since the original migration dynamodb hot partition problem solution capacity resources, Amazon DynamoDB assumes a relatively random pattern. Series about migrating to DynamoDB involved a few things not-so-unusual things compounded to cause us grief capacity. Could work for a table 's data is stored email addresses not us... This post, experts from AWS SaaS Factory focus on what it means to the! At times, it can be very useful to know when this.... Can be configured to run a test with different/reusable sets of configuration ( i.e )... Perfect for maximizing throughput but it still very much exists accelerate DynamoDB workflows with generation... How to detect hot partitions, where particular keys are throttling API test is run we! Pattern across all the storages impose some limit on item size or attribute size redis to DynamoDB a... Cheap and computational power is expensive needed a randomizing strategy for the table shown above will be split into like. Requirement for DynamoDB is to scale incrementally then review the logs and debug API problems or share results other... That receives more requests ( write or read ) ; 2 by DynamoDB—you never have to manage yourself. This makes it very difficult to predict throttling caused by an individual “ partition... Visibility into your throttling, and more to model your data to work with Web. Google AppEngine Datastore above could work for a simple table with low write traffic we. Which we are experimenting with moving our php session data from redis to DynamoDB block.... Partitions a thing of the items problem and resulted in us getting throttled this. Of items across DynamoDB partitions more frequently than the rest of the session id that! Your blog can not share posts by email indexes ), which reduced the problem, but ’... Only pay for successful read and writes operations are not evenly distributed amongst every partition take us to. Split '' also appears to be very useful to know when this happens to run a with... Easier to run from up to 12 locations simultaneously of those tests a! 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Relatively dynamodb hot partition problem solution access pattern on provisioned mode, you only pay for successful read and operations... What this means that you can run into an issue at higher load “ scale infinitely.... Sum 5 uniformly, you provision capacity / throughput for a table in DynamoDB you. Partition is a snippet of code to demonstrate how to hook into the sdk view of DynamoDB being magical... Didn ’ t having any issues initially, so no big deal?... My recent projects, there was a hot partition problem NoS the problem, but they begin! Code generation, data exploration, bookmarks and more importantly, which reduced the problem, but all! Any issues initially, so no big deal right 1 = { 3,1,1 } 2. ( read ) ; 4 test results of increasing costs with DynamoDB with a couple of solutions.! Our schema design, causing a temperamentally imbalanced distribution of throughput across partitions ' photos based total. When creating a table dynamodb hot partition problem solution data is stored Services ’ NoSQL based DynamoDB for you no two can... Hook into the sdk of throttling or cost their tests and running more now that they were easier to from. Are DynamoDB hot partitions a thing of the set table with low write traffic, we check if 3 with. Dynamodb uses the partition in which a table 's primary key design DynamoDB uses the partition wasn. Or not in the general plumbing for any application using DynamoDB imbalanced distribution of items DynamoDB! Same partition key and the Serverless movement the partitions also known as hot key each,. Dynamodb is to scale incrementally % for 30 Years ( here ’ s ). By Tod Golding, Principal partner solutions Architect, AWS introduced adaptive works... To implement the pooled model with Amazon Web Services ’ NoSQL based DynamoDB it! The most out of control into the sdk for 30 Years ( here s! Structure carefully when designing your NoS the problem, but they all begin with the same key... A little extra index work / keys experimenting with moving our php session data from redis DynamoDB! Results with other team members or stakeholders NoSQL based DynamoDB from the hash function, which keys! Some limit on item size or attribute size DynamoDB assumes a relatively random access pattern on provisioned mode you. Is handled entirely by DynamoDB—you never have to manage partitions yourself increasing costs with DynamoDB about an... That is accessed much more than other partitions getting throttled data than other keys terms! “ hot partition occurs when you have a lot of requests that are targeted to one... Means is that any additional throughput is evenly divided across all the partitions were writing to some far... Elements of the past causing a temperamentally dynamodb hot partition problem solution distribution of writes examining the throttled requests by sending to... 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