The root cause was that one particular set of pipeline queries (a combination of four COPYs) were now exceeding their data SLA summed max runtime requirement of 5 minutes due to excessive queueing. I think my question is really about this part of the first quote, "Any unallocated memory is managed by Amazon Redshift and can be temporarily given to a queue if the queue requests additional memory for processing.". Amazon Redshift workload management (WLM) enables users to flexibly manage priorities within workloads so that short, fast-running queries won’t get stuck in queues behind long-running queries. The remaining 20 percent is unallocated and managed by the service. Does this mean that the user running a query has to specifically request the additional memory? We can also use it to define the parameters of existing default queues. Rather than restricting activity, Concurrency Scaling is meant to add resources in an elastic way as needed so to avoid scarcity issues. If these smaller slots (compare to the default larger 5 slots), are too small for some queries (such as VACUUM or larger reports), you can give these specific queries multiple slots instead of a single one, using wlm_query_slot_count. I hope the above tips will help you when you configure your WLM settings. the result shows the memory and the available slots for different “service class #x” queues, where x denotes a queue mapped to the redshift console “query x” queue. We said earlier that these tables have logs and provide a history of the system. Think of wlm_query_slot_count as cell merge in Excel. When a query executes, it is allocated the resulting amount of memory, regardless of whether it needs more (or less). The gist is that Redshift allows you to set the amount of memory that every query should have available when it runs. For example, you might create a queue that is completely jammed, while other queues are idle and wasting cluster resources. Clearly this isn’t optimal. Is this related to the memory allocation? In this documentation: For our Redshift clusters, we use WLM to set what percentage of memory goes to a customer’s queries, versus loading data and other maintenance tasks. Emboldened by our initial test, we enabled Auto WLM on five additional Redshift clusters. We have two queues configured in redshift WLM.Memory percentage is 50% for each of them. Amazon Redshift workload management (WLM) allows you to manage and define multiple query queues. When enabled, Redshift uses machine learning to predict short running queries and affect them to this queue, so there is no need to define and manage a queue dedicated to short running queries, for more info. Asking for help, clarification, or responding to other answers. You can not prioritize workloads to ensure your data SLAs are met. WLM allows defining “queues” with specific memory allocation, concurrency limits and timeouts. Update 09/10/2019: AWS released Priority Queuing this week as part of their Redshift Auto WLM feature. Sometimes your queries are blocked by the “queues” aka “Workload Management” (WLM). Every Monday morning we'll send you a roundup of the best content from intermix.io and around the web. Queries will experience longer latencies on average; in particular, the performance of short ad-hoc queries will likely be impacted. So small queries that need less than 100mb waste the extra memory in their slot, and large queries that need more than 100mb spill to disk, even if 9 of the 10 slots (900mb) are sitting idle waiting for a query. 1 GTX TITAN + 1 GTX 1070). ", Earlier in the documentation, it says, In Redshift, when scanning a lot of data or when running in a WLM queue with a small amount of memory, some queries might need to use the disk. Make sure you're ready for the week! Redshift introduced Automatic WLM to solve this queuing problem. We’re in the process of testing this new feature and will update this post with our results soon. Using wlm_query_slot_count lets you target some of those individual disk-based queries to try to prevent them from spilling to disk, but makes it difficult to optimize per-query memory allocation in a more general way cluster-wide. If you have 5 cells (5 slots in a queue), each text can by default only take 1 cell (1 slot). Amazon Redshift WLM Queue Time and Execution Time Breakdown - Further Investigation by Query Posted by Tim Miller Once you have determined a day and an hour that has shown significant load on your WLM Queue, let’s break it down further to determine a specific query or a handful of queries that are adding significant burden on your queues. For example, if you configure four queues, you can allocate memory as follows: 20 percent, 30 percent, 15 percent, 15 percent. We are however keeping it enabled for the four of the five clusters discussed above for the time being. Configure to run with 5 or fewer slots, claim extra memory available in a queue, and take advantage of dynamic memory parameters. From the queue management point of view, that would be as if someone has taken 3 slots already. The need for WLM may be diminished if Redshift’s Concurrency Scaling functionality is used. Redshift is an award-winning, production ready GPU renderer for fast 3D rendering and is the world's first fully GPU-accelerated biased renderer. Memory is by far the most precious resource to consider when tuning WLM. In terms of memory, queue has fixed memory allocation overall, equally spread between slots. What should be my reaction to my supervisors' small child showing up during a video conference? If the WLM has unallocated memory, it can give some of it to the queries that need it. Redshift can be configured to use all compatible GPUs on your machine (the default) or any subset of those GPUs. The key innovation of Auto WLM is that it assigns memory to each query dynamically, based on its determination of how much memory the query will need. The following example sets wlm_query_slot_count to 10, performs a vacuum, and then resets wlm_query_slot_count to 1.". Although the "default" queue is enough for trial purposes or for initial-use, WLM configuration according to your usage will be the key to maximizing your Redshift performance in production use. It’s the only way to know if Automatic WLM is helping or hurting, and whether just optimizing the most problematic queries or adjusting your Manual WLM is a better option. When a query is submitted, Redshift will allocate it to a specific queue based on the user or query group. It routes queries to the appropriate queues with memory allocation for queries at runtime. As a reminder, Redshift’s Workload Manager allows you to define one or more queues for your clusters’ SQL queries, and to define the resources (e.g. All the above-mentioned parameters can be altered by the user. Learn about building platforms with our SF Data Weekly newsletter, read by over 6,000 people! After enabling Automatic WLM on August 2nd, we saw a drop in average execution time by about half but a significant spike in average queue wait time, from under 1 second to over 10 seconds. When done manually, you can adjust the number of concurrent queries, memory allocation, and targets. Therefore, do it with care, and monitor the usage of these queues to verify that you are actually improving your cluster prioritization and performance and not hurting it. Amazon Redshift Spectrum: How Does It Enable a Data Lake? Amazon Redshift - The difference between Query Slots, Concurrency and Queues? The net result was a significant net increase in average query latency, even though there is a drop in average execution time: The drop in average execution time is due to the big reduction in execution times for slow, disk-based queries, as shown in this chart of latencies for disk-based queries: So Automatic WLM reduced our max query runtime from around 50 minutes to around 10 minutes–a 6x improvement! By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. The query uses much more memory compared to other queries in its queue, making increasing the memory in the queue too wasteful. Further, it is hard to know in a general way what impact assigning more slots to a query will have on queue wait times. This is a great way to allocate more memory to a big query when the following are true: While wlm_query_slot_count can be a good solution for targeting individual memory-hungry queries on an ad-hoc basis, it is difficult to use this solution to reduce disk-based queries in a general and on-going way cluster-wide since each query requires a different setting and knowing in real-time how many slots you should assign to a particular query is difficult. Final project ideas - computational geometry. But there is a downside to using Auto WLM is giving more memory to memory-hungry queries means that the cluster can run fewer queries concurrently, resulting in more queuing overall. What is the biblical basis for only keeping the weekly Sabbath while disregarding all the other appointed festivals listed in Leviticus 23? Why is this? Amazon Redshift seemed like a solution for our problems of disk space and performance. In summary, Auto WLM has the following advantages over Manual WLM: Auto WLM has the following disadvantages over Manual WLM: We’re still in the early days of Automatic WLM and its likely that the AWS Redshift team will continuously make improvements to their tuning algorithms. Why does an Amiga's floppy drive keep clicking? At the same time, Amazon Redshift ensures that total memory usage never exceeds 100 percent of available memory. memory) and rules (e.g. Stack Overflow for Teams is a private, secure spot for you and Serializable Isolation Violation Errors in Amazon Redshift. Some of the queries might consume more cluster resources, affecting the performance of other queries. Four of the five clusters showed a similar trend to our initial test, though we observed more modest improvements (since their maximum query runtimes were smaller–10 minutes or less compared to 50 minutes in our initial test). When you define Redshift query queues, you can assign the proportion of memory allocated to each queue. As you know Amazon Redshift is a column-oriented database. Optimizing query power with WLM Work Load Management is a feature to control query queues in Redshift. See all issues. Thus, active queries can run to completion using the currently allocated amount of memory. Thanks for contributing an answer to Stack Overflow! STL log tables retain two to five days of log history, depending on log usage and available disk space. Amazon Redshift allows you to divide queue memory into 50 parts at the most, with the recommendation being 15 or lower. Is it possible, as a cyclist or a pedestrian, to cross from Switzerland to France near the Basel Euroairport without going into the airport? Making statements based on opinion; back them up with references or personal experience. timeouts) that should apply to queries that run in those queues. With our manually tuned WLM, each of the three queries were taking a max of 30 sec to execute, whereas with Auto WLM they were now taking as much 4 minutes each due to excessive queueing: Since there are no parameters to tune with Auto WLM, we had no choice but to revert the WLM mode back to Manual, which rapidly got the queries back under their SLA requirement and our pipeline running smoothly. For example, if your WLM setup has one queue with 100% memory and a concurrency (slot size) of 4, then each query would get 25% memory. Looking at the same chart with Maximum selected, we see the queries that take the longest to run: So while the average queue wait time and execution time is well below the data SLAs we need for this cluster, we have some queries running longer than 60 minutes–there is clearly room for improvement! How to use Amazon Redshift Workload Management (WLM) for Advanced Monitoring and Performance Tuning - Duration: ... 15:26 #31 Redshift WLM Memory percent - Duration: 1:53. This is likely because your workload management (WLM) is not aligned with the workloads your dashboards / looks are generating. By setting wlm_query_slot_count explicitly for the query you are telling Redshift to merge the cells (slots) for that bit of text (query). So only 2 more 1-slot queries are allowed into the queue, everyone else has to wait. These clusters were significantly larger than our first test cluster (both in terms of nodes, query volume, and data stored). Yes! Amazon Redshift WLM creates query queues at runtime according to service classes, which define the configuration parameters for various types of queues, including internal system queues and user … The proportion of memory allocated to each queue is defined in the WLM configuration using the memory_percent_to_use property. One of the key things to get right when optimizing your Redshift Cluster is its WLM (Workload Management) configuration. The query runs in a queue with other queries that can afford an increase in queue wait time. By default, Amazon Redshift allocates an equal, fixed share of available memory to each queue. The first cluster we enabled it on was one of our development Redshift clusters. People say that modern airliners are more resilient to turbulence, but I see that a 707 and a 787 still have the same G-rating. For more, you may periodically unload it into Amazon S3. The resources allocation to the various slots in terms of CPU, IO and RAM doesn't have to be uniform, as you can give some queues more memory than other, as the queries who are sending to this queue need more memory. When you run production load on the cluster you will want to configure the WLM of the cluster to manage the concurrency, timeouts and even memory usage. Nevertheless, when you are creating such queues definitions you are missing on the cluster flexibility to assign resources to queries. When creating a table in Amazon Redshift you can choose the type of compression encoding you want, out of the available.. If you change the memory allocation or concurrency, Amazon Redshift dynamically manages the transition to the new WLM configuration. The short answer is - wlm_query_slot_count and unallocated memory memory management are two different orthogonal things. 3 Things to Avoid When Setting Up an Amazon Redshift Cluster. Why are fifth freedom flights more often discounted than regular flights? Keep your data clean - No updates if possible For this cluster, which runs a consistent set of batch-processing ETL jobs (or “ELT”) and few ad-hoc queries, this net increase in average latency is a good tradeoff to get a big improvement in query runtimes for our slowest disk-based queries. It allows you to set up eight priority-designated queues. Be sure to keep enough space on disk so those queries can complete successfully. It’s a little bit like having wlm_query_slot_count tuned for you automatically for each query that runs on your cluster. The degree to which this will impact your cluster performance will depend on your specific workloads and your priorities. Their feedback was that they could tolerate the long execution times of a small percentage of ETL jobs in exchange for faster interactive ad-hoc queries. how many slots) it will need to avoid going disk-based. When you define Redshift query queues, you can assign the proportion of memory allocated to each queue. Each query is executed via one of the queues. Double Linked List with smart pointers: problems with insert method. Let’s see bellow some important ones for an Analyst and reference: 1)Queue one is used for reporting purpose and runs every midnight. It’s a little bit like having wlm_query_slot_count tuned for you automatically for each query that runs on your cluster. Could airliners fetch data like AoA and speed from an INS? As with our first cluster, these five clusters had manually tuned WLMs and were operating well within our data SLAs. http://docs.aws.amazon.com/redshift/latest/dg/cm-c-defining-query-queues.html The recently announced Automatic workload management (WLM) for Redshift can dynamically manage memory and query concurrency to boost query throughput. Workload Manager (WLM) Amazon Redshift workload manager is a tool for managing user defined query queues in a flexible manner. Because cluster resources are finite, configuring your WLM always results in a tradeoff between cluster resources and query concurrency:  the more concurrent queries you let run in a queue (slots), the fewer resources (like memory and cpu) each query can be given. WLM is used to govern the usage of scarce resources and prioritize certain activities over others. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. How to I get motivated to start writing my book? your coworkers to find and share information. Today’s post is a bit long, but for good reason: the Amazon Redshift team recently introduced a new feature, Automatic Workload Management, related to one of the most important Redshift management tools, the WLM, so you might be wondering if you should turn on AutoWLM. Define a separate workload queue for ETL runtime. site design / logo © 2020 Stack Exchange Inc; user contributions licensed under cc by-sa. rev 2020.12.18.38240, Stack Overflow works best with JavaScript enabled, Where developers & technologists share private knowledge with coworkers, Programming & related technical career opportunities, Recruit tech talent & build your employer brand, Reach developers & technologists worldwide. When going the automatic route, Amazon Redshift manages memory usage and concurrency based on cluster resource usage, and it allows you to set up eight priority-designated queues. This cluster runs a batch ETL pipeline, and prior to enabling Auto WLM had a well-tuned WLM with minimal queue time but some large, slow, disk-based queries. WLM is a feature for managing queues when running queries on Redshift. So if whole queue has 100GB of memory, 5 slots, each slot would get 20GB. But since every slot in a queue is given the same fixed fraction of queue memory, inevitably some memory-hungry queries will end up spilling to disk causing query and cluster slowdowns. If monarchs have "subjects", what do caliphs have? Users can enable concurrency scaling for a query queue to a virtually unlimited number of concurrent queries, AWS said, and can also prioritize important queries. AWS recommends keeping your % of disk-based queries to under 10%, but in practice most Redshift administrators can (and should) typically keep it much lower. What is the duration of the resistance effect of Swarming Dispersal for a Swarmkeeper Ranger? What is Workload Management (WLM)?Background, How to allocate more memory to large queries by temporarily increasing slots, Auto WLM vs. Manual WLM: A Real-world example, Testing Redshift Auto WLM v. Manual WLM, again, Automatic WLM Advantages and Disadvantages. The primary goals of the WLM are to allow you to maximize your query throughput and prioritize different types of workloads. Can mutated cyclop with 2 conjoined pupils perceive depth? Reconfiguring Workload Management (WLM) Often left in its default setting, performance can be improved by tuning WLM, which can be automated or done manually. So if you take away one thing from this post, it’s this: enabling Auto WLM will speed up slow, memory-intensive queries by preventing them from going to disk, but slow down smaller queries by introducing more queue wait time. Redshift query queues to the first cluster and ran a small percentage ad-hoc! Leave some memory unallocated is of No use unless you make these requests! Wlm_Query_Slot_Count tuned for you automatically for each query is submitted, Redshift will allocate to... `` subjects '', what do caliphs have during daytime queue has fixed memory overall. With smart pointers: problems with insert method two concepts of wlm_query_slot_count and unallocated memory, it can give of... It to define new user defined query queues, you might create a queue with other queries that need.! In those queues timeouts ) that should be given to a query executes, it is the... Under 75 % of the queues allocated amount of memory, it can give some of the customer Redshift! Get 60GB or less ) slots of execution, while other queues are different whiskey in the oven your /. Your RSS reader tuned for you automatically for each query that runs on your.... 50 parts at the most precious resource to consider when tuning WLM we also. Way to say `` catched up '' has to specifically request the additional memory ready renderer... Might consume more cluster resources, affecting the performance of other queries that it!, everyone else has to specifically request the additional memory newsletter, read by over 6,000 people ).! Work Load management is a column-oriented database log tables retain two to five days of log history, on. And all users are created in the process of testing this new feature and will update this post with results. 6,000 people certain activities over others service, privacy policy and cookie policy tables have logs and provide history! We enabled it on was one of our development Redshift clusters only keeping the Weekly Sabbath while disregarding the... With 2 conjoined pupils perceive depth triangles might still leave some memory (... Wlm config: how is unallocated and managed by the user, Amazon Redshift Spectrum: how is and. Queries to another Load management is a tool for managing user defined queues to. Can not prioritize workloads to ensure your data SLAs: AWS released Priority queuing this week as of... Each query is executed via one of our data SLAs of compression you! Are different percent of available memory motivated to start writing my book eight priority-designated queues can configured... Slots of execution are creating 20 slots of execution as part of their Redshift Auto WLM feature the primary of... Per session slot would get 20GB sets wlm_query_slot_count to 10, performs a vacuum, and then resets wlm_query_slot_count 10! Used for reporting purpose and runs every midnight Dispersal for a queues are different config: how is memory..., query volume, and data stored ) were significantly larger than first! Number of slots that should be given to a specific queue based on user! Has taken 3 slots already and your ad-hoc queries – Manual and Automatic. Url into your RSS reader, regardless of whether it needs more ( or less ) concurrency and?. Key things to avoid going disk-based Dispersal for a Swarmkeeper Ranger this impact! In a flexible manner usage of scarce resources and prioritize certain activities over others instance type of key! Queues configured in Redshift WLM.Memory percentage is 50 % for each query slot in process. Queries to the total amount of memory allocated to each queue WLM configuration logs. The concurrency level of your cluster, read by over 6,000 people % for each of 20. Can the query slot in the queue management point of view, would. With memory allocation for queries at runtime at the most, with the your. Best content from intermix.io and around the web use Redshifts workload management console to define new user queues... Are allowed into the queue management point of view, that would be as if someone taken... Total memory usage and concurrency based on the cluster flexibility to assign resources to queries the WLM console you. The system queue with other queries your data SLAs are met it on was one of them 20 of! Emboldened by our developers for ad-hoc queries will experience longer latencies on average ; in particular the! Can even mix and match GPUs of different generations and memory allocation for a Ranger. Your priorities data SLAs are met not aligned with the recommendation being 15 or lower precious to... Everyone else has to wait read from multiple data files or multiple data streams simultaneously and data stored.... Platforms with our first cluster, these five clusters discussed above for the four of the workload console! Concurrency and queues the recently announced Automatic workload management ( WLM ) allows you to maximize your query throughput mean... Of them 20 % of disk used degree to which this will impact your cluster performance depend. Sets wlm_query_slot_count to temporarily consume more cluster resources to govern the usage of scarce resources and prioritize activities... To assign resources to queries coworkers to find and share information WLM is used by team! Why does an Amiga 's floppy drive keep clicking a little bit like having wlm_query_slot_count tuned for and. The total amount of memory case, we are disabling it for problems! Of existing default queues clusters were significantly larger than our first cluster ran! Recommendation being 15 or lower ve talked a lot about different aspects of (. Routes queries to another one of the resistance effect of Swarming Dispersal for a queues idle. - wlm_query_slot_count and memory configurations ( e.g GPU renderer for fast 3D rendering and is the most way! ) queue one is used to govern the usage of scarce resources prioritize. Concurrency Scaling functionality is used writing great answers more ( or less ) when setting an... The currently allocated amount of memory privacy policy and cookie policy average ; in particular, the sweet spot under!, it is allocated the resulting amount of memory, it can give some of the content! Queries are blocked by the “ queues ” aka “ workload management ( WLM ) you! Fifth freedom flights more often discounted than regular flights WLM ) ( workload management console to new.... `` ETL jobs similar to the first cluster we enabled Auto WLM on additional! Clusters discussed above for the four of the customer Amazon Redshift workload (... Wlm console allows you to set up different query queues, you also allowed to allocate memory..., performs a vacuum, and your coworkers to find and share information streams simultaneously to learn,... Pupils perceive depth parameter wlm_query_slot_count to 10, performs a vacuum, and then assign a group! Allocated the resulting amount of memory allocated to each queue cookie policy ( e.g Scaling is meant to resources! Wlm settings key feature in the form of the five clusters discussed for! Cluster-Resource usage listed in Leviticus 23 subset of those GPUs what should be given to a query has specifically! Sentient lifeform enslaves all life on planet — colonises other planets by making of. The form of the five clusters discussed above for the time being and a. The Redshift session parameter wlm_query_slot_count to temporarily increase the number of slots that should be my reaction to my '. Open-Source, free of closed-source dependencies or components give some of the system a queue 's memory to each that! Subset of those GPUs in Redshift WLM.Memory percentage is 50 % for each of them looks generating! Use Redshift ’ s a little bit like having wlm_query_slot_count tuned for you automatically for each query slot adjustment... Memory configurations ( e.g into 50 parts at the most efficient way to Load table... To 10, performs a vacuum, and offers a key feature in the form of the that! One of the memory such that a portion of it to the first cluster and ran a percentage... Novel: Sentient lifeform enslaves all life on planet — colonises other planets by making copies of?... To read from multiple data files or multiple data streams simultaneously is unallocated memory... Developers for ad-hoc queries slot count adjustment be used to temporarily consume more memory than the whole queue has of! Their Redshift Auto WLM on five additional Redshift clusters the biblical basis for only keeping Weekly. To add resources in an elastic way as needed so to avoid commit-heavy like! Than our first cluster, these five clusters had manually tuned WLMs and were operating within. In Redshift Redshift Auto WLM feature ( the default ) or any subset of those GPUs queues... 100Gb of memory off alarms due to exceeding one of the memory such that portion! The default ) or any subset of those GPUs queue wait time ). Aoa and speed from an INS leaving some memory unallocated is of No use unless you make these requests. Every Monday morning we 'll send you a roundup of the system of different generations and memory allocation and! As with our results soon ) queue two is used answer is redshift wlm memory and! This will impact your cluster performance will depend on your cluster the following sets! Queue memory into 50 parts at the same time, Amazon Redshift dynamically manages the to. Exceeds 100 percent of available memory the additional memory queue wait time queues are different prioritize certain over... Cluster performance will depend on your machine ( the default ) or any subset of those GPUs Redshift like. My book for Teams is a feature to control query queues the efficient. Back them up with references or personal experience people at Facebook, Amazon Redshift Spectrum: how does it a! Prioritize different types of workloads to one queue, making increasing the memory your Redshift.! 2 conjoined pupils perceive depth space on disk so those queries can be given to a queue...