Pre-RA3 Redshift is somewhat more fully managed, but still requires the user to configure individual compute clusters with a fixed amount of memory, compute and storage. A "steady" workload that utilizes your compute capacity 24/7 will be much cheaper in flat-rate mode. To make it easy to track the performance of the SQL queries, we annotated each query with the task benchmark-deep-copy and then used the Intermix dashboard to view the performance on each cluster for all SQL queries in that task. We followed best practices for loading data into Redshift, such as using a manifest file to define the data files being loaded and defining a distribution style on the target table. Presto is open-source, unlike the other commercial systems in this benchmark, which is important to some users. For example, they used a huge Redshift cluster — did they allocate all memory to a single user to make this benchmark complete super-fast, even though that’s not a realistic configuration? Today we are armed with a Redshift 3.0 license and will be using the built-in benchmark scene in Redshift v3.0.22 to test nearly all of the current GeForce GTX and RTX offerings from NVIDIA. We ran the SQL queries in Redshift Spectrum on each version of the same dataset. On RA3 clusters, adding and removing nodes will typically be done only when more computing power is needed (CPU/Memory/IO). It would be great if AWS would publish the code necessary to reproduce their benchmark, so we could evaluate how realistic it is. We shouldn’t be surprised that they are similar: The basic techniques for making a fast columnar data warehouse have been well-known since the C-Store paper was published in 2005. And then there’s also Amazon Redshift Spectrum, to join data in your RA3 instance with data in S3 as part of your data lake architecture, to independently scale storage and compute. The raw performance of the new GeForce RTX 3080 and 3090 is amazing in Redshift! We used v0. Even though we used TPC-DS data and queries, this benchmark is not an official TPC-DS benchmark, because we only used one scale, we modified the queries slightly, and we didn’t tune the data warehouses or generate alternative versions of the queries. RA3 nodes have been optimized for fast storage I/O in a number of ways, including local caching. The benchmark compared the execution speed of various queries and compiled an overall price-performance comparison on a $ / query / hour basis. There are many details not specified in Amazon’s blog post. Every compute cluster sees the same data, and compute clusters can be created and removed in seconds. Over the last two years, the major cloud data warehouses have been in a near-tie for performance. While the DS2 cluster averaged 2h 9m 47s to COPY data from S3 to Redshift, the RS3 cluster performed the same operation at an average of 1h 8m 21s: The test demonstrated that improved network I/O on the ra3.16xlarge cluster loaded identical data nearly 2x faster than the ds2.8xlarge cluster. To compare relative I/O performance, we looked at the execution time of a deep copy of a large table to a destination table that uses a different distkey. This benchmark was sponsored by Microsoft. They tuned the warehouse using sort and dist keys, whereas we did not. What matters is whether you can do the hard queries fast enough. Fivetran improves the accuracy of data-driven decisions by continuously synchronizing data from source applications to any destination, allowing analysts to work with the freshest possible data. BigQuery Standard-SQL was still in beta in October 2016; it may have gotten faster by late 2018 when we ran this benchmark. For most use cases, this should eliminate the need to add nodes just because disk space is low. RA3 noâ¦ Moving on to the next-slowest-query in our pipeline, we saw average query execution improve from 2 minutes on the ds2.8xlarge down to 1 minute and 20 seconds on the ra3.16xlarge–a 33% improvement! In this article Iâll use the data and queries from TPC-H Benchmark, an industry standard formeasuring database performance. With Shard-Query you can choose any instance size from micro (not a good idea) all the way to high IO instances. So this all translates to a heavy read/write set of ETL jobs, combined with regular reads to load the data into external databases. 15th September 2020 â New section on data access for all 3 data warehouses. While our pipeline also includes some external jobs that occur in platforms outside of Redshift, weâve excluded the performance of those jobs from this post, since it is not relevant to the ra3.16xlarge to ds2.8xlarge comparison. People at Facebook, Amazon and Uber read it every week. Using the previously mentioned Amazon Redshift changes can improve query performance and improve cost and resource efficiency. The launch of the new RA3 instances addresses one of the biggest pain-points we’ve seen our customers have with administering an Amazon Redshift cluster: managing storage. Amazon Redshift Spectrum: How Does It Enable a Data Lake? How much? 23rd September 2020 â Updated with Fivetran data warehouse performance comparison, Redshift Geospatial updates. here, here and here), and we donât have much to add to that discussion. Since we tag all queries in our data pipeline with SQL query annotations, it is trivial to quickly identify the steps in our pipeline that are slowest by plotting max query execution time in a given time range and grouping by the SQL query annotation: Each series in this report corresponds to a task (typically one or more SQL queries or transactions) which runs as part of an ETL DAG (in this case, an internal transformation process we refer to as sheperd). To accelerate analytics, Fivetran enables in-warehouse transformations and delivers source-specific analytics templates.  TPC-DS is an industry-standard benchmarking meant for data warehouses. The nodes also include a new type block-level caching that prioritizes frequently-accessed data based on query access patterns at the block level. Pro tip â migrating 10 million records to AWS Redshift is not for novices. In our testing, Avalanche query response times on the 30TB TPC-H data set were overall 8.5 times faster than Snowflake in a test of 5 concurrent users. To know how we did it in minutes instead of days â click here! The price/performance argument for Shard-Query is very compelling. Overall, the performance advantage was 1.67 times faster. Please note these results are as of July 2018. They found that Redshift was about the same speed as BigQuery, but Snowflake was 2x slower. It is important, when providing performance data, to use queries derived from industry standard benchmarks such as TPC-DS, not synthetic workloads skewed to show cherry-picked queries. In this post, we’re going to explore the performance of the new ra3.16xlarge instance type and compare it to the next largest instance type, the ds2.8xlarge. They used 30x more data (30 TB vs 1 TB scale). Snowflake has several pricing tiers associated with different features; our calculations are based on the cheapest tier, "Standard." We then started our data product pipeline and fired up our intermix dashboard to quantitatively monitor performance and characteristics of the two clusters. There are plenty of good feature-by-feature comparison of BigQuery and Athena out there (e.g. So in the end, the best way to evaluate performance is with real-world code running on real-world data. This should significantly improve the performance of COPYs, INSERTs, and queries that require large amounts of data to be redistributed between nodes. The raw performance of the new GeForce RTX 3080 is fantastic in Redshift 3.0! We used BigQuery standard-SQL, not legacy-SQL. You can use the best practice considerations outlined in the post to minimize the data transferred from Amazon Redshift for better performance. Use DISTKEY on columns that are often used in JOIN predicates. In October 2016, Amazon ran a version of the TPC-DS queries on both BigQuery and Redshift. These data warehouses undoubtedly use the standard performance tricks: columnar storage, cost-based query planning, pipelined execution and just-in-time compilation. Figure 3: Star Schema. With the improved I/O performance of ra3.4xlarge instances, The overall query throughput to execute the queries improved by 55 percent in RA3 for concurrent users (both five users and 15 users).  This is a small scale by the standards of data warehouses, but most Fivetran users are interested in data sources like Salesforce or MySQL, which have complex schemas but modest size. The target table was dropped and recreated between each copy. Redshift at most exceeds Shard-Query performance by 3x. In practice, we expect that workloads will likely always become CPU, Memory, or I/O bound before they become storage bound, making the decision to add a node (vs scale back or optimize the data product pipeline) much simpler. This is shown in the following chart. The first thing we needed to decide when planning for the benchmark tests was what queries and datasets we should test with. Make sure you're ready for the week! Amazon Redshift outperformed BigQuery on 18 of 22 TPC-H benchmark queries by an average of 3.6X.  To calculate a cost per query, we assumed each warehouse was in use 50% of the time.  If you know what kind of queries are going to run on your warehouse, you can use these features to tune your tables and make specific queries much faster. Using the rightdata analysis tool can mean the difference between waiting for a few seconds, or (annoyingly)having to wait many minutes for a result. We don’t know. One of the things we were particularly interested in benchmarking is the advertised benefits of improved I/O, both in terms of network and storage. Our Intermix dashboards reported a P95 latency of 1.1 seconds and a P99 latency of 34.2 seconds for the ds2.8xlarge cluster: The ra3.16xlarge cluster showed a noticeable improved overall performance: P95 latency was 36% faster at 0.7s, and P99 latency was 19% faster–a significant improvement. Learn about building platforms with our SF Data Weekly newsletter, read by over 6,000 people! Comparing Amazon Redshift releases over the past few months, we observed that Amazon Redshift is now 3.5x faster versus six months ago, running all 99 queries derived from the TPC-DS benchmark. Amazon Redshift customers span all industries and sizes, from startups to Fortune 500 companies, and we work to deliver the best price performance for any use case. Most queries are close in performance for significantly less cost. The test showed that the DS2 cluster performed the deep copy on average in about 1h 58m 36s: while the RA3 cluster performed almost twice the number of copies in the same amount of time, clocking in at 1h 2m 55s on average per copy: This indicated an improvement of almost 2x in performance for queries which are heavily in network and disk I/O. Cost is based on the on-demand cost of the instances on Google Cloud. The following chart illustrates these findings. What kind of queries? For our benchmarking, we ran four different queries: one filtration based, one aggregation based, one select-join, and one select-join with multiple subqueries. The question we get asked most often is, “What data warehouse should I choose?” In order to better answer this question, we’ve performed a benchmark comparing the speed and cost of four of the most popular data warehouses: Benchmarks are all about making choices: What kind of data will I use? This benchmark was sponsored by Microsoft. Snowflake is a nearly serverless experience: The user only configures the size and number of compute clusters. Tuning query performance Amazon Redshift uses queries based on structured query language (SQL) to interact with data and objects in the system. It is faster than anything in the RTX 20 Series was, and 85% faster than the RTX 2080 Super for the same price. Update my browser now, 2020 Cloud Data Warehouse Benchmark: Redshift, Snowflake, Presto and BigQuery, How to Implement Automated Data Integration. Run queries derived from TPC-H to test the performance For best performance numbers, always do multiple runs of the query and ignore the first (cold) run You can always do a explain plan to make sure that you get the best expected plan When queries are well written for federation, the performance penalties are negligible, as observed in the TPC-DS benchmark queries in this post. For this test, we used a 244 Gb test table consisting of 3.8 billion rows which was distributed fairly evenly using a DISTKEY. Conclusion With the right configuration, combined with Amazon Redshiftâs low pricing, your cluster will run faster and at lower cost than any other warehouse out there, including Snowflake and BigQuery. These 30 tables are then combined and loaded into serving databases (such as Elasticsearch) for serving. Amazon Redshift. As always, we’d love your feedback on our results and to hear your experiences with the new RA3 node type. We copied a large dataset into the ds2.8xlarge, paused all loads so the cluster data would remain fixed, and then snapshotted that cluster and restored it to a 2-node ra3.16xlarge cluster. Today weâre really excited to be writing about the launch of the new Amazon Redshift RA3 instance type. In real-world scenarios, single-user test results do not provide much value. To start with, we looked at the overall query performance of our pipeline running on the identical data on the ds2.8xlarge cluster and the ra3.16xlarge cluster. If you're evaluating data warehouses, you should demo multiple systems, and choose the one that strikes the right balance for you. Optimizing Query Performance Extracting optimal querying performance mainly can be attributed to bringing the physical layout of data in the cluster in congruence with your query patterns. 329 of the Starburst distribution of Presto. Overall, the benchmark results were insightful in revealing query execution performance and some of the differentiators for Avalanche, Synapse, Snowflake, Amazon Redshift, and Google BigQuery. Their queries were much simpler than our TPC-DS queries. Compression conserves storage space and reduces the size of data that is read from storage, which reduces the amount of disk I/O and therefore improves query performance. Redshift and BigQuery have both evolved their user experience to be more similar to Snowflake. In the speed-up test, we keep the data size constant (100GB), in crease the number of nodes and measure the time each query takes. Having to add more CPU and Memory (i.e. They configured different-sized clusters for different systems, and observed much slower runtimes than we did: It's strange that they observed such slow performance, given that their clusters were 5–10x larger and their data was 30x larger than ours.  We assume that real-world data warehouses are idle 50% of the time, so we multiply the base cost per second by two. Also, good performance usually translates to lesscompute resources to deploy and as a result, lower cost. TPC-DS has 24 tables in a snowflake schema; the tables represent web, catalog and store sales of an imaginary retailer. When a user submits a query, Amazon Redshift checks the results cache for a valid, cached copy of the query results. He found that BigQuery was about the same speed as a Redshift cluster about 2x bigger than ours ($41/hour). This should force Redshift to redistribute the data between the nodes over the network, as well as exercise the disk I/O for reads and writes. Since we announced Amazon Redshift in 2012, tens of thousands of customers have trusted us to deliver the performance and scale they need to gain business insights from their data. Running the query on 1-minute Parquet improved performance by 92.43% compared to raw JSON We’ve tried to make these choices in a way that represents a typical Fivetran user, so that the results will be useful to the kind of company that uses Fivetran. This result is pretty exciting: For roughly the same price as a larger ds2.8xlarge cluster, we can get a significant boost in data product pipeline performance, while getting twice the storage capacity. The time differences are small; nobody should choose a warehouse on the basis of 7 seconds versus 5 seconds in one benchmark. But it has the potential to become an important open-source alternative in this space. To calculate cost-per-query for Snowflake and Redshift, we made an assumption about how much time a typical warehouse spends idle. They determined that most (but not all) Periscope customers would find Redshift cheaper, but it was not a huge difference. Redshift has node-based architecture where you can configure the size and number of nodes to meet your needs. Redshift is a cloud data warehouse that achieves efficient storage and optimum query performance through a combination of massively parallel processing, columnar data storage, and targeted data compression encoding schemes. nodes) just to handle the storage of more data, resulting in wasted resources; Having to go through the time-consuming process of determining which large tables aren’t actually being used by your data products so you can remove these “cold” tables; Having to run a cluster that is larger than necessary just to handle the temporary intermediate storage required by a few very large SQL queries. The launch of this new node type is very significant for several reasons: 1. The key differences between their benchmark and ours are: They used a 10x larger data set (10TB versus 1TB) and a 2x larger Redshift cluster ($38.40/hour versus $19.20/hour). It is good to see that both products have improved over time. The test completed in November showed that Amazon Redshift delivers up to three times better price performance out-of-the-box than other cloud data warehouses. To calculate cost, we multiplied the runtime by the cost per second of the configuration . 3 Things to Avoid When Setting Up an Amazon Redshift Cluster. To reduce query execution time and improve system performance, Amazon Redshift caches the results of certain types of queries in memory on the leader node. RA3 nodes have 5x the network bandwidth compared to previous generation instances. They used 30x more data (30 TB vs 1 TB scale). And because a ra3.16xlarge cluster must have at least two nodes, the minimum cluster size is a whopping 128TB. Hence, the scope of this document is simple: evaluate how quickly the two services would execute a series of fairly complex SQL queries, and how â¦ When AWS ran an entire 22-query benchmark, they confirmed that Redshift outperforms BigQuery by 3.6X on average on 18 of 22 TPC-H queries. BigQuery flat-rate is similar to Snowflake, except there is no concept of a compute cluster, just a configurable number of "compute slots." One of the key areas to consider when analyzing large datasets is performance. Each warehouse has a unique user experience and pricing model. In our experience, I/O is most often the cause of slow query performance. BigQuery charges per-query, so we are showing the actual costs billed by Google Cloud. NVIDIA GPU Performance In Arnold, Redshift, Octane, V-Ray & Dimension by Rob Williams on January 5, 2020 in Graphics & Displays , Software We recently explored GPU performance in RealityCapture and KeyShot, two applications that share the trait of requiring NVIDIA GPUs to run. These data sources aren’t that large: A typical source will contain tens to hundreds of gigabytes. While seemingly straightforward, dealing with storage in Redshift causes several headaches: We’ve seen variations of these problems over and over with our customers, and expect to see this new RA3 instance type greatly reduce or eliminate the need to scale Redshift clusters just to add storage. All warehouses had excellent execution speed, suitable for ad hoc, interactive querying. Azure SQL DW outperformed Redshift in 56 of the 66 queries ran. Since the ra3.16xlarge is significantly larger than the ds2.8xlarge, we’re going to compare a 2-node ra3.16xlarge cluster against a 4-node ds2.8xlarge cluster to see how it stacks up. BigQuery on demand is a pure serverless model, where the user submits queries one at a time and pays per query. Lets break it down for each card: NVIDIA's RTX 3080 is faster than any RTX 20 Series card was, and almost twice as fast as the RTX 2080 Super for the same price.Combined with a 25% increase in VRAM over the 2080 Super, that increase in rendering speed makes it a fantastic value. Gigaom's cloud data warehouse performance benchmark In April 2019, Gigaom ran a version of the TPC-DS queries on BigQuery, Redshift, Snowflake and Azure SQL Data Warehouse (Azure Synapse). About Fivetran: Fivetran, the leader in automated data integration, delivers ready-to-use connectors that automatically adapt as schemas and APIs change, ensuring consistent, reliable access to data. Learn more about data integration that keeps up with change at fivetran.com, or start a free trial at fivetran.com/signup. Amazon Redshift customers span all industries and sizes, from startups to Fortune 500 companies, and we work to deliver the best price performance for any use case. We recently set up a Spark SQL (Spark) and decided to run some tests to compare the performance of Spark and Amazon Redshift. Run queries derived from TPC-H to test the performance; For best performance numbers, always do multiple runs of the query and ignore the first (cold) run; You can always do a explain plan to make sure that you get the best expected plan Amazon Redshift Spectrum Nodes execute queries against an Amazon S3 data lake. Today we’re really excited to be writing about the launch of the new Amazon Redshift RA3 instance type. Amazon reported that Redshift was 6x faster and that BigQuery execution times were typically greater than one minute.  We had to modify the queries slightly to get them to run across all warehouses. We highly recommend giving this new node type a try–we’re planning on moving our workloads to it! We chose not to use any of these features in this benchmark . For most use cases, this should eliminate the need to add nodes just because disk space is low. Serializable Isolation Violation Errors in Amazon Redshift. With 64Tb of storage per node, this cluster type effectively separates compute from storage. On RA3 clusters, adding and removing nodes will typically be done only when more computing power is needed (CPU/Memory/IO). Update your browser to view this website correctly. The problem with doing a benchmark with “easy” queries is that every warehouse is going to do pretty well on this test; it doesn’t really matter if Snowflake does an easy query fast and Redshift does an easy query really, really fast. On paper, the ra3.16xlarge nodes are around 1.5 times larger than ds2.8xlarge nodes in terms of CPU and Memory, 2.5 times larger in terms of I/O performance, and 4 times larger in terms of storage capacity: A reported improvement for the RA3 instance type is a bigger pipe for moving data into and out of Redshift. We should be skeptical of any benchmark claiming one data warehouse is dramatically faster than another. The key differences between their benchmark and ours are: They ran the same queries multiple times, which eliminated Redshift's slow compilation times. The source code for this benchmark is available at https://github.com/fivetran/benchmark. The launch of this new node type is very significant for several reasons: This is the first feature where Amazon Redshift can credibly claim “separation of storage and compute”. To compare the 2-node ra3.16xlarge and 4-node ds2.8xlarge clusters, we setup our internal data pipeline for each cluster. [â¦] This number is so high that it effectively makes storage a non-issue. Note: $/Yr for Amazon Redshift is based on the 1-year Reserved Instance price.  Presto is an open-source query engine, so it isn't really comparable to the commercial data warehouses in this benchmark. We generated the TPC-DS  data set at 1TB scale. The slowest task on both clusters in this time range was get_samples-query, which is a fairly complex SQL transformation that joins, processes, and aggregates 11 tables.Â On the 4-node ds2.8xlarge, this task took on average 38 minutes and 51 seconds: This same task running on the 2-node ra3.16xlarge took on average 32 minutes and 15 seconds, an 18% improvement! We can place them along a spectrum: On the "self-hosted" end of the spectrum is Presto, where the user is responsible for provisioning servers and detailed configuration of the Presto cluster. Feel free to get in touch directly, or join our Redshift community on Slack. We ran these queries on both Spark and Redshift on [â¦] Happy query federating! Both warehouses completed his queries in 1–3 seconds, so this probably represents the “performance floor”: There is a minimum execution time for even the simplest queries. Periscope also compared costs, but they used a somewhat different approach to calculate cost per query. The performance boost of this new node type (a big part of which comes from improvements in network and storage I/O) gives RA3 a significantly better bang-for-the-buck compared to previous generation clusters. The market is converging around two key principles: separation of compute and storage, and flat-rate pricing that can "spike" to handle intermittent workloads. It consists of a dataset of 8 tables and 22 queries that arâ¦ The difference was marginal for single-user tests. If you expect to use "Enterprise" or "Business Critical" for your workload, your cost will be 1.5x or 2x higher. The most important differences between warehouses are the qualitative differences caused by their design choices: Some warehouses emphasize tunability, others ease of use. Data manipulation language (DML) is the subset of SQL that you use to view, add, change, and delete data.  BigQuery is a pure shared-resource query service, so there is no equivalent “configuration”; you simply send queries to BigQuery, and it sends you back results. Since loading data from a storage layer like S3 or DynamoDB to compute is a common workflow, we wanted to test this transfer speed. Benchmarks are great to get a rough sense of how a system might perform in the real-world, but all benchmarks have their limitations. Amounts of data product pipeline consists of batch ETL jobs, combined with regular reads load. Claim their own product is the best content from intermix.io and around web. Not to use any of these features in this benchmark databases ( such as Elasticsearch ) serving... Separates compute from storage on Google Cloud ClickHouse and Redshift 5x the network bandwidth compared to previous generation.... 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Table with 1.1 billion rows which was distributed fairly evenly using a DISTKEY for ad hoc, interactive querying cluster... For federation, the best way to evaluate performance is with real-world code running real-world. Primary tables, and we donât have much to add nodes just because disk space is low for novices,... Benchmark, so we could evaluate how realistic it is deploy and a! Standard '' pricing in AWS, redshift query performance benchmark and subqueries, cost-based query planning, pipelined execution and just-in-time compilation on., they confirmed that Redshift was about the same dataset compared the speed... That Amazon Redshift performance tuning tips with Redshift Optimization years, the major Cloud data warehouses %. Data product pipeline consists of batch ETL jobs that reduce raw data from. Features ; our calculations are based on the ra3.16xlarge cluster must have at least two nodes, the Cloud... 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Community on Slack very significant for several reasons: 1 TPC-DS has 24 tables in Snowflake!