Continuous improvement

Continuous monitoring

Data security

Versatility

DJ Otter

<p>In 2013, Andy Pavlo started the OtterTune research project at Carnegie Mellon University (CMU) as an extension to the <a href="https://db.cs.cmu.edu/projects/oltp-bench/">OLTP-Bench project</a>. Recognizing the importance and complexity of DBMS configuration tuning, The National Science Foundation (NSF) funded the project with a <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423210">$500K grant</a> in 2014. That same year, <a href="https://www.cs.cmu.edu/~dvanaken/">Dana Van Aken</a>, who is now our co-founder and CTO, started as a new PhD student. She became the primary researcher for the project.</p>
<p>For the next seven years, we worked on the research that led to the commercial release of OtterTune this <a href="https://www.zdnet.com/article/ottertune-sets-out-to-auto-tune-all-the-databases/">past May</a>. The insights of our work have been published in several peer-reviewed papers. In a pair of blog posts, we’ll share an account of the research and findings that led us to where we are today. Here are some of the highlights and lessons we learned during the early years, developing OtterTune’s machine learning algorithms and services architecture.</p>
<h2>Reusing Training Data to Improve the Efficacy of Tuning Recommendations</h2>
<p>The first paper about the OtterTune service, presented at SIGMOD in 2017, was <a href="https://db.cs.cmu.edu/papers/2017/p1009-van-aken.pdf"><strong>Automatic Database Management System Tuning Through Large-scale Machine Learning</strong></a>.</p>
<p>The paper covered our automated, machine learning-driven approach, leveraging past experience and collecting new information to tune DBMS configurations. In our research, we used a combination of supervised and unsupervised machine learning methods to:</p>
<ol>
<li>Select the most impactful knobs,</li>
<li>Map unseen database workloads to previous workloads so we can transfer experience, and</li>
<li>Recommend knob settings.</li>
</ol>
<p>We implemented our techniques in OtterTune and tested it on three DBMSs. Our evaluation showed that OtterTune recommends configurations that are as good as or better than ones generated by existing tools or a human expert.</p>
<p>We designed OtterTune to use a pipeline of different machine learning algorithms (purple shapes in the diagram below) to: 1) characterize the target database's workload, 2) identify which knobs to tune, and 3) recommend new configuration settings. The OtterTune service uses ML metrics to map a new database workload it's trying to optimize to a similar workload that the service has seen in the past. This is how OtterTune can reuse training data it has collected from tuning other databases to improve the efficacy of its recommendations.</p>
<p><em>The OtterTune machine learning pipeline:</em>
<img src="//images.contentful.com/bhyalf3qnjwn/5hMA8WZiL9kUvXSBz9tPXz/3fd6d8fff81666c568436248858c51d3/OtterTune_Machine_Learning_Pipeline.jpeg" alt="OtterTune Machine Learning Pipeline"></p>
<h4>Key Learnings and Advancements:</h4>
<p>For this research, <strong>we ran 100,000 trials</strong> to build the initial training data corpus for OtterTune. Each trial was benchmarked using OLTP-Bench. As our ML algorithms improved, we found that we did not need to collect as much training data.</p>
<p>Among other ML algorithms, OtterTune uses a <a href="https://en.wikipedia.org/w/index.php?title=Gaussian_process_regression">Gaussian Process Regression</a>  algorithm, a common approach for solving black-box optimization problems and hyperparameter tuning. We originally wrote the algorithm in MATLAB. During a summer internship at CMU, Bohan Zhang, one of OtterTune’s co-founders, then <strong>rewrote it in TensorFlow</strong> and improved its computing efficiency, resulting in a 5x performance increase.</p>
<p>The first version of <strong>OtterTune supported MySQL, PostgreSQL, and Actian Vector</strong>. We evaluated both OLTP and OLAP workloads. We were using the latest versions of MySQL (v5.6) and PostgreSQL (v9.3) at the time, and we learned that the earlier DBMS versions had fewer knobs for improving performance of OLAP workloads than OLTP workloads.</p>
<p>OtterTune was well-received in academia after presenting it at SIGMOD 2017, but it wasn’t until we were invited to write a <a href="https://aws.amazon.com/blogs/machine-learning/tuning-your-dbms-automatically-with-machine-learning/"><strong>guest post for the AWS Machine Learning blog</strong></a> that we started hearing from a lot of industry folks trying to solve similar problems.</p>
<h2>Automatically Selecting a Configuration that Improves DBMS Performance</h2>
<p>A year later, we presented a short paper, <a href="http://www.vldb.org/pvldb/vol11/p1910-zhang.pdf"><strong>A Demonstration of the OtterTune Automatic Database Management System Tuning Service</strong></a>, at the VLDB conference in Brazil.</p>
<p>The paper described OtterTune’s microservice architecture. The tuning agent (now called the Driver) was used to collect the target DBMS’s current configuration and metrics and then transmit them to the OtterTune tuning manager. The tuning manager stored this data in its internal repository of previous tuning sessions. The algorithm then trains models with the data from the target DBMS as well as previous tuning data from similar workloads to produce a new configuration recommendation. The tuning manager sent this new configuration back to the agent to install on the target DBMS.</p>
<h4>Key Learnings and Advancements:</h4>
<p>For the live demonstration at VLDB, we challenged conference attendees to tune PostgreSQL (v9.6) running the TPC-C benchmark and compared them against OtterTune’s tuning algorithm. We gave them a web interface that allowed them to set the values for several knobs and then ran the workload.</p>
<p>OtterTune was able to get <strong>20% better performance than the best configuration from a human</strong>.</p>
<p><img src="//images.ctfassets.net/bhyalf3qnjwn/2h9o2pVYtI5zUSA4gbRzqi/cfb528dbb652388ca3cf143606075b20/Bohan-Zhang-OtterTune-VLDB-2018.jpeg" alt="Bohan-Zhang-OtterTune-VLDB-2018">
<em>Bohan Zhang in action at VLDB 2018. View other <a href="https://twitter.com/andy_pavlo/status/1035179003198488577">VLDB 2018 photos</a>.</em></p>
<h2>Stay Tuned for Part 2</h2>
<p>When we started the R&#x26;D on OtterTune in 2013, we knew we were onto something big. Our next step was to test it out in real-world settings. In our follow-on blog post, we’ll share what we learned from our first customer deployments of OtterTune.</p>


7 years of OtterTune R&D Part I - the Lab Years

Dana Van Aken

<p>Companies are moving their databases to hosted database services, such as Amazon RDS, to take advantage of the flexibility and scalability of cloud deployments. But what people do not often realize is that while these “fully managed” services simplify database provisioning and maintenance, customers are still responsible for some critical administrative tasks that are not easily automated. Database configuration tuning is one of them.</p>
<p>Optimizing a database’s configuration knobs for the workload speeds up performance and scales your systems, thereby lowering costs. Amazon already tweaks a few of MySQL and PostgreSQL out-of-the-box default settings for its RDS deployments based on the instance type. However, they do not tune them for any specific workload.</p>
<p>OtterTune automates the process of optimizing a database’s configuration knobs for its specific workload by using machine learning (ML). We showed in a <a href="https://ottertune.com/blog/rds-postgres-tuning-benchmark">previous post</a> that OtterTune achieves a 3x performance improvement for a PostgreSQL database by tuning its configuration for its workload versus using the default configuration from Amazon RDS.</p>
<p>The OtterTune ML algorithms make decisions based on their training data, and sometimes these decisions can be unpredictable because the algorithms do not interpret the world the same way humans do. For this reason, OtterTune has safeguards in place to ensure that ML does not harm your database.</p>
<p>We have learned a lot about how an ML-based DBMS tuner fits an organization’s established database administration policies and practices. These experiences have led us to develop operational settings that allow humans to control aspects of the automated tuning process. This article discusses which configuration knobs OtterTune can tune and the features available to users for managing this.</p>
<h2>Types of Knobs</h2>
<p>A DBMS has hundreds of configuration knobs that control aspects of its runtime operations. These knobs typically fall into one of the following categories:</p>
<ol>
<li>Resources</li>
<li>Policies</li>
<li>Locations</li>
</ol>
<h4>Resources</h4>
<p>Knobs in this category specify how much of a resource the system uses for a task. These can be either for fixed components (e.g., <a href="https://www.postgresql.org/docs/current/runtime-config-autovacuum.html#GUC-AUTOVACUUM-MAX-WORKERS">number of vacuum workers</a>) or for dynamic activities (e.g., <a href="https://www.postgresql.org/docs/current/runtime-config-resource.html#GUC-WORK-MEM">amount of memory to use per query</a>).</p>
<p>Examples of resource knobs for MySQL and PostgreSQL:</p>
<table>
<thead>
<tr>
<th>DBMS</th>
<th>Knob</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>MySQL</td>
<td><a href="https://dev.mysql.com/doc/refman/8.0/en/innodb-parameters.html#sysvar_innodb_buffer_pool_size">innodb_buffer_pool_size</a> (fixed component)</td>
<td>The size in bytes of the buffer pool, the memory area where InnoDB caches table and index data.</td>
</tr>
<tr>
<td>PostgreSQL</td>
<td><a href="https://www.postgresql.org/docs/13/runtime-config-resource.html">work_mem</a> (dynamic activity)</td>
<td>Sets the base maximum amount of memory to be used by a query operation (such as a sort or hash table) before writing to temporary disk files.</td>
</tr>
</tbody>
</table>
<h4>Policies</h4>
<p>These configuration knobs control how the DBMS behaves for specific tasks. For example, a knob can control whether the DBMS flushes the write-ahead log to disk when a transaction commits.</p>
<p>Examples of policy knobs for MySQL and PostgreSQL:</p>
<table>
<thead>
<tr>
<th>DBMS</th>
<th>Knob</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>MySQL</td>
<td><a href="https://dev.mysql.com/doc/refman/8.0/en/innodb-parameters.html#sysvar_innodb_flush_method">innodb_flush_method</a></td>
<td>Defines the method used to flush data to InnoDB data files and log files, which can affect I/O throughput.</td>
</tr>
<tr>
<td>PostgreSQL</td>
<td><a href="https://www.postgresql.org/docs/13/runtime-config-wal.html">wal_compression</a></td>
<td>When this parameter is on, the PostgreSQL server compresses a full page image written to WAL.</td>
</tr>
</tbody>
</table>
<h4>Locations</h4>
<p>Lastly, location knobs specify where the DBMS finds resources that it needs (e.g., file paths) and how it interacts with the outside world (e.g., network port number).</p>
<p>Examples of location knobs for MySQL and PostgreSQL:</p>
<table>
<thead>
<tr>
<th>DBMS</th>
<th>Knob</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>MySQL</td>
<td><a href="https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_port">port</a></td>
<td>The number of the port on which the server listens for TCP/IP connections.</td>
</tr>
<tr>
<td>PostgreSQL</td>
<td><a href="https://www.postgresql.org/docs/13/runtime-config-file-locations.html">config_file</a></td>
<td>Specifies the main server configuration file (customarily called postgresql.conf).</td>
</tr>
</tbody>
</table>
<h2>Paws off! Controlling Which Knobs OtterTune Configures</h2>
<p>We allow OtterTune to tune most knobs that control resources and policies. However, there are a few knobs that can have negative consequences like potentially causing the DBMS to lose data. We do not let OtterTune tune these knobs.</p>
<p>These restrictions are to avoid exogenous problems that are not readily measurable by the service. For example, the algorithms will learn that turning off disk writes for transaction commits makes the DBMS run faster. But now, if the system crashes, it might lose data because it no longer flushes data to the disk. The ML algorithm has no way of knowing whether that is a problem because that is an external cost that it cannot measure. A human has to make that value judgment. Thus, we disallow OtterTune from making those types of changes to a DBMS’s configuration.</p>
<p>OtterTune will not try to tune your port number or any other location knob for obvious reasons.</p>
<p>Examples of disallowed knobs for MySQL:</p>
<table>
<thead>
<tr>
<th>Knob</th>
<th>Why it's disallowed</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://dev.mysql.com/doc/refman/8.0/en/innodb-parameters.html#sysvar_innodb_doublewrite">innodb_doublewrite</a></td>
<td>Controls whether the doublewrite buffer is enabled. You might consider disabling the doublewrite buffer if you are more concerned with performance than data integrity, as may be the case when performing benchmarks, for example.</td>
</tr>
<tr>
<td><a href="https://dev.mysql.com/doc/refman/8.0/en/innodb-parameters.html#sysvar_innodb_flush_log_at_trx_commit">innodb_flush_at_trx_commit</a></td>
<td>Controls the balance between strict ACID compliance for commit operations and higher performance that is possible when commit-related I/O operations are rearranged and done in batches. You can achieve better performance by changing the default value but then you can lose transactions in a crash.</td>
</tr>
</tbody>
</table>
<p>Examples of disallowed knobs for PostgreSQL:</p>
<table>
<thead>
<tr>
<th>Knob</th>
<th>Why it's disallowed</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://www.postgresql.org/docs/13/runtime-config-wal.html#GUC-FSYNC">fsync</a></td>
<td>If this parameter is on, the PostgreSQL server will try to make sure that updates are physically written to disk, by issuing fsync() system calls. While turning off fsync is often a performance benefit, this can result in unrecoverable data corruption in the event of a power failure or system crash.</td>
</tr>
<tr>
<td><a href="https://www.postgresql.org/docs/13/runtime-config-wal.html#GUC-FULL-PAGE-WRITES">full_page_writes</a></td>
<td>When this parameter is on, the PostgreSQL server writes the entire content of each disk page to WAL during the first modification of that page after a checkpoint. Turning this parameter off speeds normal operation, but might lead to either unrecoverable data corruption, or silent data corruption, after a system failure.</td>
</tr>
</tbody>
</table>
<h2>Knob value ranges are controlled too</h2>
<p><img src="//images.contentful.com/bhyalf3qnjwn/17QcOd9pzRsB4CRsdDz5Fd/352fa94654689e156c128c1c8b040abc/OtterTune-Allowed-Knob-Value-Ranges.png" alt="OtterTune Allowed Knob Value Ranges"></p>
<p>OtterTune also suggests a permitted range of values for each knob. This range is important for knobs that allocate system resources since setting a knob larger than what’s available can cause the database to crash. OtterTune collects hardware information from the user’s deployment and computes the suggested ranges based on the amount of a resource available.</p>
<p>OtterTune limits the range of some policy knobs as well. For example, many knobs that specify timeouts are safe to tune but only within a specific range.</p>
<p>Users can configure which knobs to tune and their ranges when connecting a database to the service for the first time. Once setup is complete, users can modify their settings from the options panel.</p>
<h2>Try OtterTune yourself</h2>
<p>If you'd like to learn more about how OtterTune automates database configuration tuning, watch this <a href="https://ottertune.com/blog/ottertune-explained-in-five-minutes">short video</a>.</p>
<p>If you'd like to see whether OtterTune can improve your database performance, let us know, and we'll get you started with OtterTune.</p>
<p><a href="https://ottertune.com/try"><strong>Try OtterTune...</strong></a></p>


How to Prevent Machine Learning From Wrecking Your Database

Bohan Zhang

<p><a href="https://aws.amazon.com/rds/postgresql/">Amazon RDS PostgreSQL</a> makes deploying and operating a PostgreSQL database instance easy. When you provision an RDS PostgreSQL instance, Amazon launches it with their <a href="https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appendix.PostgreSQL.CommonDBATasks.html#Appendix.PostgreSQL.CommonDBATasks.Parameters">default configuration settings</a> for dozens of tunable "knobs." These knob settings affect database performance, and the default settings are often not optimal for your database's specific workload.</p>
<p>Tuning the knob settings in order to optimize price-performance for your database requires:</p>
<ol>
<li>Specialized knowledge of PostgreSQL internals</li>
<li>Time to iteratively make changes and test the impact of the change on performance until it is optimized (i.e., trial-and-error) </li>
</ol>
<p>If you run Amazon RDS PostgreSQL and you lack (1) or (2) above, or if your workload changes over time (thus requiring re-tuning), you'll likely pay higher cloud costs than you ought to for your database.</p>
<h4>Putting Amazon RDS PostgreSQL knob settings to the test</h4>
<p>The question you’re probably asking is “how far off the mark could my Amazon RDS knob settings be?”</p>
<p>The question you’re probably not asking (yet) is “Can machine learning auto-tune my database knob settings to optimize its performance?”</p>
<p>To help answer those questions, we ran an experiment to test Amazon RDS PostgreSQL performance with three database configurations:
a)	Default Amazon RDS knob settings
b)  Knob setting recommendations from <a href="https://pgtune.leopard.in.ua/#/">PGTune</a>
c)  Knob setting recommendations from OtterTune's <a href="https://ottertune.com/features">machine learning algorithms</a></p>
<p>To run the test, we used the <a href="https://github.com/oltpbenchmark/oltpbench">OLTP-bench</a> framework to run the <a href="http://www.tpc.org/tpcc/">TPC-C benchmark</a>, a well-known transactional workload. We collected runtime metrics from the database, and calculated the throughput based on those metrics. Specifically, for Amazon RDS PostgreSQL, we optimized for the <a href="https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-DATABASE-VIEW"><strong>xact_commit</strong></a> metric (the number of transaction commits in the database).</p>
<p>We ran the tests on an Amazon db.m5.4xlarge RDS instance equipped with:</p>
<ul>
<li>16vCPUs,</li>
<li>64GB RAM, and</li>
<li>4000 provisioned IOPs.</li>
</ul>
<p>For PGTune, we applied the configuration recommended by PGTune to the database:</p>
<pre><code>max_connections = 300
shared_buffers = 16GB
effective_cache_size = 48GB
maintenance_work_mem = 2GB
checkpoint_completion_target = 0.9
wal_buffers = 16MB
default_statistics_target = 100
random_page_cost = 1.1
effective_io_concurrency = 200
work_mem = 13981kB
min_wal_size = 2GB
max_wal_size = 8GB
max_worker_processes = 16
max_parallel_workers_per_gather = 4
max_parallel_workers = 16
max_parallel_maintenance_workers = 4
</code></pre>
<h4>Result: OtterTune more than doubles performance (throughput)</h4>
<p><img src="//images.ctfassets.net/bhyalf3qnjwn/59VRT4Y7XhwYqGFTyhrluF/b0fae005e05c36c4a1f50f8fac0ad91f/ottertune-throughput-results.png" alt="ottertune-throughput-results"></p>
<p>In this test, the configuration changes that PGTune recommended increased throughput (transactions per second) by 41.9%.</p>
<p>OtterTune’s machine learning increased throughput over 3x more than PGTune...146.7% greater than the default RDS settings.</p>
<h4>Double the performance, or half the cost?</h4>
<p>In the previous test, we showed that using machine learning to find the optimal configuration knob settings more than doubled database performance. That’s a nice outcome if you’re seeking faster database performance.</p>
<p>For people who are satisfied with database performance, but would like to lower costs, knob-tuning can also help. To demonstrate that, we re-ran the test on a smaller RDS instance costing half as much, a db.m5.2xlarge equipped with:</p>
<ul>
<li>8vCPUs</li>
<li>32GB RAM</li>
<li>2000 provisioned IOPs</li>
</ul>
<p>Optimizing Amazon RDS PostgreSQL on the db.m5.2xlarge instance enabled it to achieve the same performance as PostgreSQL (with the default settings) on the twice-as-large db.m5.4xlarge, but at half the instance cost:</p>
<p><img src="//images.ctfassets.net/bhyalf3qnjwn/4DvKA6lO8vA656BHXhZdwM/da2ce6208a2e4f4406217f1c0a5b3bb5/ottertune-instance-cost.png" alt="ottertune-instance-cost"></p>
<h4>How long does it take OtterTune to find “optimal?”</h4>
<p>OtterTune works by iterating through different knob setting configurations and observing the database’s performance. Its machine learning models are trained to make educated decisions about new configuration changes for the observed workload and optimization goals. It’s similar to the approach an expert DBA would take, but it performs it faster.</p>
<p>The iterations required to find optimal performance in the second test are shown below. From the first iteration to the final iteration it took 2 hours.</p>
<p><img src="//images.ctfassets.net/bhyalf3qnjwn/4I3dVsgAW0RtQRkfgOmsGP/4cda3175418545cca0f0a16b7cff348f/ottertune-time-savings.png" alt="ottertune-time-savings"></p>
<p>In addition, OtterTune will continue to monitor the database workload and automatically adjust knob settings if the workload changes.</p>
<h4>Try it yourself</h4>
<p>If you'd like to learn more about how OtterTune automates database configuration tuning, watch <a href="https://ottertune.com/blog/ottertune-explained-in-five-minutes">this short video</a>.</p>
<p>If you'd like to see whether OtterTune can improve your database performance, let us know, and we'll get your started with OtterTune.</p>
<p>[<a href="https://ottertune.com/try">Try OtterTune</a>]</p>


Benchmark: Using Machine Learning to Optimize Amazon RDS PostgreSQL Performance

Andy Pavlo

<h4>So many knobs, so little time</h4>
<p>Modern databases have hundreds of configuration “knobs” that affect the performance of the database. It’s becoming impractical for humans to know how to tune all these knobs for a single database let alone tens or hundreds of databases.</p>
<p><a href="https://ottertune.com/features">OtterTune</a> solves this problem by using machine learning to automate database configuration tuning, which results in better database price-performance.</p>
<h4>OtterTune automatic database tuning explained</h4>
<p>In this five-minute video, we explain how OtterTune works:</p>
<ol>
<li>Connecting OtterTune to your database - no special software or access to user data is required.</li>
<li>Monitoring your database’s performance and collecting settings and metrics.</li>
<li>Training machine learning models to predict how your database’s performance will change as its configuration knobs change.</li>
<li>Automatically installing the new configuration and monitoring its effect.</li>
<li>Continually monitoring your database to make sure that it is always using the most optimal configuration.</li>
</ol>
<div class="max-w-2xl">
      <div class="aspect-w-16 aspect-h-9">
        <iframe src="https://www.youtube.com/embed/SJdnpAMI_q4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
      </div>
    </div>
<h4>Watching OtterTune tune an Amazon RDS PostgreSQL database</h4>
<p>The video also walks you through a demonstration in which OtterTune tunes an Amazon RDS PostgreSQL database configured with the default RDS settings and running a write-heavy transactional (TPC-C) workload.</p>
<p>After several minutes you’ll see that OtterTune has generated the first new configuration and installed it in the database. After several more iterations you’ll see that the OtterTune-optimized database achieves 87% higher throughput than the original configuration.</p>
<h4>Can OtterTune improve your DBMS performance? Try it out!</h4>
<p>If you’d like to try OtterTune on your database(s), <a href="https://ottertune.com/try">let us know</a>, and we’ll help you get started.</p>


Watch: OtterTune Explained in 5 Minutes...

<h4>What is OtterTune?</h4>
<p>OtterTune is automatic database tuning software. It uses machine learning to automatically tune the configuration knobs of your database to achieve the best performance possible for its current workload. OtterTune can keep your database tuned as the workload changes over time. The goal of the service is to reduce your operating costs and make life easier for DBAs.</p>
<h4>What is a configuration knob?</h4>
<p>A “knob” is a configuration parameter that a database exposes that can control some aspect of its runtime behavior, such as buffer pool sizes, caching policies, and other features. Most databases have lots of knobs. There are over <a href="https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html">500 knobs in MySQL</a> and <a href="https://www.postgresql.org/docs/current/runtime-config.html">300 knobs in PostgreSQL</a>.</p>
<h4>What data does OtterTune analyze?</h4>
<p>OtterTune collects configuration knobs (e.g., the size of the buffer pool or redo log) and runtime metrics (e.g., the number of transactions executed or dirty pages in the buffer pool) from the target database, nothing else. It does not access any sensitive application or user data.</p>
<h4>What databases does OtterTune support today?</h4>
<p>As of May 2021, OtterTune supports the following databases running on-premises or via Amazon RDS:</p>
<ul>
<li>PostgreSQL (versions 9.6, 10, 11, 12, 13)</li>
<li>MySQL (versions 5.7 and 8.0)</li>
</ul>
<h4>What databases will OtterTune support next?</h4>
<p>As of May 2021, OtterTune is adding support for MariaDB and Amazon Aurora (MySQL and PostgreSQL). If you have a database or platform you’d like OtterTune to support or want an update on our supported DBMS list, please contact us.</p>
<h4>How long does it take to optimize a database?</h4>
<p>Many factors will affect how long it takes for OtterTune to generate a better configuration for your database. Some of these factors include:</p>
<ul>
<li>How many knobs is the service tuning?</li>
<li>How complex is the workload?</li>
<li>How often does the service try a new configuration?</li>
<li>How many databases that OtterTune has tuned in the past that are similar to the new database?</li>
</ul>
<p>Database tuning is a complex problem. With that said, in practice, we have found that OtterTune produces some initial improvement within hours.</p>
<h4>Does OtterTune tune queries or indexes?</h4>
<p>As of May 2021, OtterTune only analyzes and adjusts database configuration settings. It does not analyze or recommend changes to queries, indexes, or database design.</p>
<h4>Does OtterTune plan to automate other DBMS operations in the future?</h4>
<p>OtterTune was founded by researchers at <a href="https://db.cs.cmu.edu/">Carnegie Mellon University</a> working on autonomous and self-driving databases. We have spent over a decade working on DBMS automation, so yes, we plan to extend OtterTune to support automating more DBMS operations over time.</p>
<h4>Does OtterTune restart my database to implement config changes?</h4>
<p>Some knob settings require a restart to take effect. OtterTune will only tune knobs that require a restart if you allow it to. Otherwise, OtterTune will only adjust “dynamic” knobs that take effect without restarting the database.</p>
<p>Note that some of the knobs that require a restart are often important to tune. That is, they have a significant impact on performance when tuned correctly for some applications. Given this, you can control OtterTune’s restart policy, setting it to restart the database any time, or only during specific time windows, or never (it will just advise you to restart manually).</p>
<h4>Does OtterTune require me to install software or make changes to my application?</h4>
<p>Nope. OtterTune runs on its own as a separate service and connects and interfaces with the target database to collect metrics and change settings via standard SQL. You do not need to install any additional third-party extensions in your database.</p>
<h4>What access privileges does OtterTune require?</h4>
<p>The privileges that OtterTune needs depend on the database system, service provider, and tuning options. OtterTune requires user credentials that it uses to connect to the database. This user only has permissions to retrieve metrics and not any sensitive user data. For Amazon RDS, additional information, such as the database identifier, region, and login permissions, is required. OtterTune can be configured to either tune or only monitor your database. If you enable tuning, you can also decide whether to allow OtterTune to restart the database and provide OtterTune with the correct permissions.</p>
<h4>How much does OtterTune cost?</h4>
<p>There’s a setup fee to get started with OtterTune. If you run OtterTune 24x7 to keep your database optimized continuously, there’s a small monthly fee. Please contact us if you need more pricing details for your situation.</p>
<h4>How do I get started?</h4>
<p>Press the “Try OtterTune” button. After that, we’ll have a quick chat with you to help you pick a good first database to optimize with OtterTune and get you set up and running.</p>
<h4>What’s with the otters?</h4>
<p>Well, we auto-tune databases. So, auto-tune...OtterTune. Get it? Plus, otters are cute. But sometimes aggressive, which should only worry you if you’re a seal, sea lion, or beaver.</p>


OtterTune Frequently Asked Questions

<p>Yo. Today is a banner day that follows years of research on autonomous database technology and machine learning at <a href="https://db.cs.cmu.edu">Carnegie Mellon University</a> (CMU) and lessons learned from customer deployments. We’re <a href="https://www.prnewswire.com/news-releases/ottertune-launches-to-improve-database-performance-through-automated-database-configuration-tuning-301289311.html?tc=eml_cleartime">happy to announce</a> the first commercial release of <a href="https://ottertune.com/features">OtterTune</a> software-as-a-service (SaaS) and seed funding From <a href="https://accel.com/">Accel</a>. Our mission is to make databases run better, with less effort, and at a lower cost.</p>
<p>With hundreds of configuration settings (“knobs”), the work required to keep a database tuned, or more often, hundreds of databases, exceeds human abilities. Based on our research at CMU, we knew that, with the help of machine learning (ML), we could achieve better results with an automated tuning service that would learn from each database and become more efficient over time.</p>
<p>Here’s a little bit of our history to help you learn how we got here today and where we’re going next.</p>
<h3>University Research</h3>

<p>OtterTune started in 2014 as a research project in CMU’s Computer Science Department led by OtterTune co-founders <a href="https://www.cs.cmu.edu/~pavlo">Andy Pavlo</a> and <a href="https://www.cs.cmu.edu/~dvanaken/">Dana Van Aken</a>. We were studying how to use ML to devise strategies to optimize DBMS knobs for any application automatically. We published the <a href="https://db.cs.cmu.edu/papers/2017/p1009-van-aken.pdf">first academic paper on OtterTune</a> at the SIGMOD conference in 2017 and presented it in Chicago.</p>
<p>With the development help of co-founder <a href="https://bohanjason.github.io">Bohan Zhang</a> (first as a summer intern from PKU, then later a CMU MS student), we built the first versions of the OtterTune database tuning service, which uses ML to generate and install optimized DBMS configurations. OtterTune observes the DBMS’s workload through its metrics and then trains recommendation models that select better knob values. It then reuses these models to tune other DBMSs faster.</p>
<h3>Our First Customers</h3>
<p>The first real-world application of OtterTune in a production environment was at <a href="https://www.societegenerale.com/en">Société Générale</a> (SG). SG approached us to conduct a field study involving their fleet of Postgres databases. The first thing we discovered was that they had an Oracle database that was more problematic, so that became our first project. At that time, OtterTune had been built to work with MySQL and PostgreSQL. But we extended it to support Oracle and discovered that our ML models worked on Oracle well enough--recommending a config that increased throughput by 45% and saved them the not-insignificant cost of several Oracle core licenses.</p>
<p>[<a href="https://db.cs.cmu.edu/papers/2021/p1241-aken.pdf">Read the research paper we published with Société Générale</a>]</p>
<p>We also worked with another customer and their large cluster of MySQL databases. OtterTune helped them reduce their CPU utilization and the number of running threads by more than 20%. At their scale, this reduction in utilization resulted in the ability to turn off multiple machines and save tons in server costs.</p>
<p>Besides refining our ML models with these experiences, we learned a lot about fitting in with database administration tasks and policies. This effort led to the development of new operational settings in the OtterTune service, which allow humans to control when and how frequently OtterTune runs, if and when it can restart databases with new configs, which knobs are off-limits and allowed value ranges.</p>
<h3>A Group of Otters is Called a "Raft"</h3>
<p>We are not doing this alone. Our raft is impressive. A few years ago, Amazon invited us to publish a <a href="https://aws.amazon.com/blogs/machine-learning/tuning-your-dbms-automatically-with-machine-learning/">blog post</a> about OtterTune. That caught peoples’ attention, including early investors with valuable experience launching data management companies like Databricks, Snowflake, Cockroach Labs, SingleStore, Vertica, PingCap, and others. Eventually, we teamed up with Accel to fund the launch of OtterTune and our next phase of growth and development. Our friends at Accel (<a href="https://accel.com/people/ping-li">Ping Li</a> and <a href="https://www.accel.com/people/daniel-levine">Daniel Levine</a>) are guiding us through the early stages of our company and helping us realize our big ideas. Daniel notes that purple sea urchins are an invasive species that otters eat, and that helps keep kelp forests healthy. Needless to say, Accel gets otters.</p>
<h3>Join us...the water is fine, and the fish are tasty</h3>
<p>If you’ve got databases, we’ve got a way to keep them tuned and always optimized for you. We work with PostgreSQL, MySQL running in the cloud (Amazon RDS), or self-managed instances. The more databases OtterTune works with, the smarter it gets, so let us know if you'd like to <a href="https://ottertune.com/try">https://ottertune.com/try</a></p>
<p>You’ll also probably also learn more than you ever wanted to know about otters. Spoiler: Otters are vicious animals.</p>

Database optimization. On autopilot.

Continually improve your database
performance, automatically.

Let's face it: Manual database optimization is a pain

Three steps to better database performance:

1Connect

2Collect

3Optimize

OtterTune supports the most popular databases and cloud platforms

Why OtterTune?

How you benefit from automatic database tuning

Continuous improvement

Continuous monitoring

Data security

Versatility

Knowledge center

7 years of OtterTune R&D Part I - the Lab Years

How to Prevent Machine Learning From Wrecking Your Database

Benchmark: Using Machine Learning to Optimize Amazon RDS PostgreSQL Performance

Watch: OtterTune Explained in 5 Minutes...

Ready to put your database optimization on autopilot?

Database optimization. On autopilot.

Continually improve your database performance, automatically.

Let's face it: Manual database optimization is a pain

Three steps to better database performance:

1Connect

2Collect

3Optimize

OtterTune supports the most popular databases and cloud platforms

Why OtterTune?

How you benefit from automatic database tuning

Continuous improvement

Continuous monitoring

Data security

Versatility

Knowledge center

7 years of OtterTune R&D Part I - the Lab Years

How to Prevent Machine Learning From Wrecking Your Database

Benchmark: Using Machine Learning to Optimize Amazon RDS PostgreSQL Performance

Watch: OtterTune Explained in 5 Minutes...

Ready to put your database optimization on autopilot?

Continually improve your database
performance, automatically.