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Take an Interactive Tour of OtterTune

Click on the beacons, tooltips or next buttons to learn more about OtterTune. You can also use the PRODUCT TOUR button in the lower-left corner to jump to different sections of OtterTune.

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By default, OtterTune shows you an overview of your entire fleet of databases.

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You can click here to switch between AWS accounts that are connected to OtterTune.

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In this example, we can see there are two AWS accounts connected to OtterTune.

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The Health Score is made up of the metrics (databases, resources, tables, indexes and queries) across all your databases in your fleet. It's a good way to assess whether you overall database health is holding steady, or getting better or worse over time.

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Let's click on Score History to see how the Health Score has changed over the last two weeks.

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You can click on any point in time over this two-week period to see the overall Health Score, as well as a breakdown by relevant metrics.

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As you use OtterTune, we'll log hours and dollars saved. These savings are based on what it would take a senior data engineer to research and apply new knob values.

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Let's dive into relevant Action Items.

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There are multiple pages of Action Items. On the third page, we can see there are recommendations to review for two databases and the OtterTune Agent needs to be upgraded for one database. Let's see what other Action Items there are.

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On this second page, we can see some databases need their settings updated, one database is missing the OtterTune Agent and one database has 13 recommendations to review.

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Here, you can review all the databases within an AWS account and fleet.

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We provide a brief summary of the database name, DBMS type, whether it's a cluster or instance and the size or configuration.

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You can see the region tied to the particular instance.

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Here we provide a quick snapshot of whether the databases is enabled for use with OtterTune and if the agent is working correctly. OtterTune works off database credits (1 credit allows you to connect to one database). These credits are swappable, so you can deactivate a database and reuse that freed-up credit on another database.

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This is the Health Score for the individual instance.

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Here, you can see how many recommendations a database has to review.

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Let's now tour the Database Instance Dashboard.

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We're now in a database-specific dashboard. You can see info like the name, region, DBMS, instance type and configuration, etc.

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This drop-down menu allows you to quickly switch between dashboards for different database instances in your fleet.

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You can see how clicking expands the list to show the other databases.

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This provides an overview on whether the database is read or write heavy, analysis on peak time and stats on read/write IOPs.

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Just like in the Fleet Dashboard, you can see your Health Score for the day and over the last two weeks.

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Clicking Database Settings allows you to check the OtterTune Agent connection status and its version number, and disable the database if needed.

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This area showcases how well OtterTune has optimized your database toward your target objective as compared to the baseline. When starting OtterTune, the AI must get through eight sets of knob configurations to learn your workload and find a best configuration. In this example, only one more configuration is needed and OtterTune is 88% trained.

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Once you train OtterTune's AI, you'll arrive at your Best Configuration and we'll show you the improvement over baseline for your target objective. In this example, P99 query latency has been improved or reduced by 74%.

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You can click on any knob configuration to see what knobs were impacted and their values. More on that shortly.

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This is a brief overview of some of the knobs we recommend tuning. We provide names, values and an explanation of the knob. All recommended knob changes can be found in the Recommendations section.

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Here, we flag some of the unused and duplicate indexes detected. All of them can be found in the Recommendations section.

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This is a brief overview of some of the queries we've flagged. All are found in the Recommendations section.

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Let's dive into the Recommendations section to explore how we analyze and tune knobs, queries, indexes and other areas.

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The Recommendations section looks at knobs, indexes, queries, tables, database health and autovacuum (Postgres only).

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You'll see a list of your recommendations, complete with the knob, the change in its value and a definition of the knob.

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If you're on auto-tuning, knobs will be updated automatically. For manual review tuning, you can click the Apply Selected Recommendations button to update your knobs.

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For self-directed tuning, you can click the Copy Command Lines button to get the SQL code you need to update the knobs yourself.

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This is an example of the code supplied to allow you to update the knobs yourself.

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We can see all the knob configurations that either OtterTune or the user applied over time. It is sorted with most recent at the top. In this example, you can see P99 query latency has been getting better with each tuning.

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Let's see what happens if we expand that latest tuning or knob configuration.

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We can now see all the knob names, their prior values and their current values, as well as the value of the target objective (P99 query latency).

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We're done looking at knobs. Let's check our index recommendations.

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Now we're looking at index recommendations. OtterTune checks for unused and duplicate indexes. It does not recommend indexes to add.

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OtterTune sorts by index name, DB and schema, table and size (biggest to smallest). You can resort by size.

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You click checkboxes to select indexes and, like knobs, then click a Copy SQL Command option to get the SQL code to delete those indexes.

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OtterTune provides the code to remove the relevant indexes you've selected.

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Duplicate indexes are also flagged and like unused indexes, can be sorted be size.

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The lock icon indicates OtterTune has flagged a duplicate index as a primary or unique index and does not recommend deleting it.

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Just like with unused indexes, you can click the checkbox for duplicate indexes you want to remove and then get the SQL code to delete them.

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Clicking See All Indexes allows you to see not just indexes flagged by OtterTune as unused or duplicates, but all indexes.

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Inside the All Indexes view, you see the same type of info in the prior screen, plus index scans and tuple reads, and can constrain search results to a particular date range.

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Let's check out query recommendations.

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OtterTune looks for anti-patterns (poorly-written code) in queries and suggests improvements. We flag any queries that take over 1,000 milliseconds (ms) or 1 second to execute. You can sort by number of calls, average time to execute and total time spent executing.

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You can see we've sorted the results by slowest time to execute. Let's look at this query.

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OtterTune does not rewrite queries, but it does provide suggestions on how to improve them and provides the query digest ID and code for reference.

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We now on page two of suggestions. Let's look at another query.

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In this case, we have a rather large set of JOINs and OtterTune is recommending creating an index to speed up the JOINs.

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Let's dive into tables.

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OtterTune checks for dead tuples, stale statistics and excessive reads. If there are issues, you'll see a red icon and everything is good, it will be green.

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You can dive deeper into autovacuum (Postgres only) and sort by any of the column headers, e.g., live tuples, dead tuple rate, etc.

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You can also look at IO stats and sort by column headers like sequential scans, buffer cache hit rate, etc.

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Let's look at general database recommendations.

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The Database section is a great area to go to see if you're over or under provisioned and for opportunities to optimize your database(s).

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In this example, OtterTune has flagged temporary disk data, as well as connection and memory utilization as issues. It has noted that the first two can be improved via knob settings and query recommendations OtterTune provides. The third indicates they database could need more memory, but tuning it will help with memory utilization.

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OtterTune also points out when things are going well. We can see that CPU utilization and disk space are OK and autovacuum management is efficient.

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Let's look at the autovacuum section.

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OtterTune looks for stuck autovacuums and excessive scans.

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If you have an autovacuum running, it will show up in this section.

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Past autovacuums will show up here.

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Let's dive into analytics in the Performance Charts section.

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In this area, you can create charts for metrics, knobs and target objectives across 100+ values. All OtterTune users get access to unlimited historical data and chart creation.

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In this example, we have a chart for P99 query latency for the last 24 hours.

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If we click on Show configurations, that adds tick marks and icons to the chart to denote when new knob configurations were applied to the database so you can see their impact.

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Our standard reports are in increments of 3, 12 and 24 hours, as well as 3 days, but you can also create a report for any date range.

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Let's try creating a new chart for CPU utilization.

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We'll name our chart and select the Metric chart type.

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We'll select CPUUtilization. You can see there are lots of values to select from.

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And just like that — near instantly — we have our new chart and it's tied to the last 24 hours and shows our configurations on the chart.

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The Configurations section is where the current and all past knob configurations are stored. Let's check it out.

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The Configurations section allows you to review all knob configurations and their impact on your target objective(s).

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You can sort by date or target objective.

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For any configuration, you can review all the knobs and their values.

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Configurations can be compared to one another to see the differences in knob values, as well as how the target objective was impacted. In this example, configuration 162406 improved P99 latency 4% compared to configuration 162401.

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If you ever need to revert to a prior configuration, you can click this button.

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Let's check out the last section of OtterTune: Tuning Options.

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Tuning Options is where the core settings of OtterTune are housed.

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OtterTune offers three tuning modes for knobs. All the knobs OtterTune optimizes are dynamic, so they do not require database restarts. We'll go over the three tuning modes below.

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Auto-tuning is the recommended tuning mode as it helps train the AI faster and gets your best configuration quicker. In it, OtterTune will automatically apply knob updates in the background — there's no need for you to do anything! We'll send you emails about updates.

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Manual review tuning is for those that want more control. OtterTune will surface its knob recommendations and you can approve them.

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Self-directed tuning is for those that want maximum control. You will have to review OtterTune's knob recommendations and copy and paste SQL code to apply them yourself.

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Important: To train OtterTune's AI faster and get more performance and cost-saving gains quicker and save you time, we recommend users start with auto-tuning or quickly pivot to it after getting comfortable with OtterTune.

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For your target objective(s), you can optimize at the database instance or cluster level. More on target objectives shortly.

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For RDS, Amazon exposes knobs via parameter groups and you must create a new, non-default parameter group. If you try to tune a default parameter group, OtterTune's AI will not be available. More on this is available in our docs.

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Here, you can define when OtterTune is allowed to apply new knob configurations. This can be used to constrain tuning away from suboptimal times, e.g., outside business hours or during peak usage. You can add as many tuning periods as you like.

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Let's try adding a tuning period to our schedule.

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We pick the day of the week.

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Let's do midnight to 5 a.m. on Monday and hit the save (disk) icon.

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We can see the new tuning period has been added. Any tuning period can be copied or deleted.

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This is where you pick a target objective. This tells the OtterTune AI what is most important to optimize. You can have different target objectives for each database or tune your cluster toward the same one. We recommend P99 query latency (as optimizing it tends to give the best bang for your buck). If you need additional target objectives like read/write IOPs, contact support@ottertune.com.

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This is a list of recommended knobs that OtterTune tunes by default. The knobs will vary on whether the database is Postgres or MySQL and the AWS environment is Aurora or RDS. This example is showing your Postgres on RDS. The vast majority of performance gains will come from tuning these knobs.

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By default, all these recommended knobs will be tuned, but you can deselect and select specific ones. Our Allowed Values are safe bounds we have found for optimization. If you need these values changed, reach out to support@ottertune.com. We do not recommend changing the allowed values.

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OtterTune also gives you the ability to optimize additional knobs. By default, these are off as we have found most customers do not need them, but you're welcome to select some or all to see if they give additional performance gains. You can always consult with us by emailing support@ottertune.com if you have questions about these or the recommended knobs.

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This chat bubble in the lower-right corner will allow you to chat with support in real time or submit a ticket. For all users, OtterTune offers support via chat, email, Slack or video calls.

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Optimize Your Databases With OtterTune

Thanks for touring OtterTune! If you want to review any section, click the PRODUCT TOUR button in the lower-left corner. You can review our documentation here. The buttons below will allow you to start your free trial (and reviewing pricing and plans) or contact sales if you have questions.

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