210 lines
9.2 KiB
Markdown
210 lines
9.2 KiB
Markdown
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---
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title: InfluxDB compared to SQL databases
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menu:
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influxdb_1_5:
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name: Comparing InfluxDB to SQL databases
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weight: 30
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parent: Concepts
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---
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# What's in a database?
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This page gives SQL users an overview of how InfluxDB is like an SQL database and how it's not.
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It highlights some of the major distinctions between the two and provides a loose crosswalk between the different database terminologies and query languages.
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## In general...
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InfluxDB is designed to work with time-series data.
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SQL databases can handle time-series but weren't created strictly for that purpose.
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In short, InfluxDB is made to store a large volume of time-series data and perform real-time analysis on those data, quickly.
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### Timing is everything
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In InfluxDB, a timestamp identifies a single point in any given data series.
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This is like an SQL database table where the primary key is pre-set by the system and is always time.
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InfluxDB also recognizes that your [schema](/influxdb/v1.5/concepts/glossary/#schema) preferences may change over time.
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In InfluxDB you don't have to define schemas up front.
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Data points can have one of the fields on a measurement, all of the fields on a measurement, or any number in-between.
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You can add new fields to a measurement simply by writing a point for that new field.
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If you need an explanation of the terms measurements, tags, and fields check out the next section for an SQL database to InfluxDB terminology crosswalk.
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## Terminology
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The table below is a (very) simple example of a table called `foodships` in an SQL database
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with the unindexed column `#_foodships` and the indexed columns `park_id`, `planet`, and `time`.
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``` sql
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+---------+---------+---------------------+--------------+
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| park_id | planet | time | #_foodships |
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+---------+---------+---------------------+--------------+
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| 1 | Earth | 1429185600000000000 | 0 |
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| 1 | Earth | 1429185601000000000 | 3 |
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| 1 | Earth | 1429185602000000000 | 15 |
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| 1 | Earth | 1429185603000000000 | 15 |
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| 2 | Saturn | 1429185600000000000 | 5 |
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| 2 | Saturn | 1429185601000000000 | 9 |
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| 2 | Saturn | 1429185602000000000 | 10 |
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| 2 | Saturn | 1429185603000000000 | 14 |
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| 3 | Jupiter | 1429185600000000000 | 20 |
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| 3 | Jupiter | 1429185601000000000 | 21 |
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| 3 | Jupiter | 1429185602000000000 | 21 |
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| 3 | Jupiter | 1429185603000000000 | 20 |
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| 4 | Saturn | 1429185600000000000 | 5 |
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| 4 | Saturn | 1429185601000000000 | 5 |
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| 4 | Saturn | 1429185602000000000 | 6 |
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| 4 | Saturn | 1429185603000000000 | 5 |
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+---------+---------+---------------------+--------------+
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```
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Those same data look like this in InfluxDB:
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```sql
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name: foodships
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tags: park_id=1, planet=Earth
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time #_foodships
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---- ------------
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2015-04-16T12:00:00Z 0
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2015-04-16T12:00:01Z 3
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2015-04-16T12:00:02Z 15
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2015-04-16T12:00:03Z 15
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name: foodships
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tags: park_id=2, planet=Saturn
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time #_foodships
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---- ------------
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2015-04-16T12:00:00Z 5
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2015-04-16T12:00:01Z 9
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2015-04-16T12:00:02Z 10
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2015-04-16T12:00:03Z 14
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name: foodships
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tags: park_id=3, planet=Jupiter
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time #_foodships
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---- ------------
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2015-04-16T12:00:00Z 20
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2015-04-16T12:00:01Z 21
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2015-04-16T12:00:02Z 21
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2015-04-16T12:00:03Z 20
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name: foodships
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tags: park_id=4, planet=Saturn
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time #_foodships
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---- ------------
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2015-04-16T12:00:00Z 5
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2015-04-16T12:00:01Z 5
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2015-04-16T12:00:02Z 6
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2015-04-16T12:00:03Z 5
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```
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Referencing the example above, in general:
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* An InfluxDB measurement (`foodships`) is similar to an SQL database table.
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* InfluxDB tags ( `park_id` and `planet`) are like indexed columns in an SQL database.
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* InfluxDB fields (`#_foodships`) are like unindexed columns in an SQL database.
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* InfluxDB points (for example, `2015-04-16T12:00:00Z 5`) are similar to SQL rows.
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Building on this comparison of database terminology,
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InfluxDB's [continuous queries](/influxdb/v1.5/concepts/glossary/#continuous-query-cq)
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and [retention policies](/influxdb/v1.5/concepts/glossary/#retention-policy-rp) are
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similar to stored procedures in an SQL database.
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They're specified once and then performed regularly and automatically.
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Of course, there are some major disparities between SQL databases and InfluxDB.
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SQL `JOIN`s aren't available for InfluxDB measurements; your schema design should reflect that difference.
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And, as we mentioned above, a measurement is like an SQL table where the primary index is always pre-set to time.
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InfluxDB timestamps must be in UNIX epoch (GMT) or formatted as a date-time string valid under RFC3339.
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For more detailed descriptions of the InfluxDB terms mentioned in this section see our [Glossary of Terms](/influxdb/v1.5/concepts/glossary/).
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## InfluxQL and SQL
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InfluxQL is an SQL-like query language for interacting with InfluxDB.
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It has been lovingly crafted to feel familiar to those coming from other
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SQL or SQL-like environments while also providing features specific
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to storing and analyzing time series data.
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InfluxQL's `SELECT` statement follows the form of an SQL `SELECT` statement:
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```sql
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SELECT <stuff> FROM <measurement_name> WHERE <some_conditions>
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```
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where `WHERE` is optional.
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To get the InfluxDB output in the section above, you'd enter:
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```sql
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SELECT * FROM "foodships"
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```
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If you only wanted to see data for the planet `Saturn`, you'd enter:
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```sql
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SELECT * FROM "foodships" WHERE "planet" = 'Saturn'
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```
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If you wanted to see data for the planet `Saturn` after 12:00:01 UTC on April 16, 2015, you'd enter:
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```sql
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SELECT * FROM "foodships" WHERE "planet" = 'Saturn' AND time > '2015-04-16 12:00:01'
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```
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As shown in the example above, InfluxQL allows you to specify the time range of your query in the `WHERE` clause.
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You can use date-time strings wrapped in single quotes that have the
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format `YYYY-MM-DD HH:MM:SS.mmm`
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( `mmm` is milliseconds and is optional, and you can also specify microseconds or nanoseconds).
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You can also use relative time with `now()` which refers to the server's current timestamp:
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```sql
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SELECT * FROM "foodships" WHERE time > now() - 1h
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```
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That query outputs the data in the `foodships` measure where the timestamp is newer than the server's current time minus one hour.
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The options for specifying time durations with `now()` are:
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|Letter|Meaning|
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|:---:|:---:|
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| ns | nanoseconds |
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|u or µ|microseconds|
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| ms | milliseconds |
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|s | seconds |
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| m | minutes |
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| h | hours |
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| d | days |
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| w | weeks |
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<br/>
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InfluxQL also supports regular expressions, arithmetic in expressions, `SHOW` statements, and `GROUP BY` statements.
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See our [data exploration](/influxdb/v1.5/query_language/data_exploration/) page for an in-depth discussion of those topics.
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InfluxQL functions include `COUNT`, `MIN`, `MAX`, `MEDIAN`, `DERIVATIVE` and more.
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For a full list check out the [functions](/influxdb/v1.5/query_language/functions/) page.
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Now that you have the general idea, check out our [Getting Started Guide](/influxdb/v1.5/introduction/getting-started/).
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## A note on why InfluxDB isn't CRUD...
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InfluxDB is a database that has been optimized for time series data.
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This data commonly comes from sources like distributed sensor groups, click data from large websites, or lists of financial transactions.
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One thing this data has in common is that it is more useful in the aggregate.
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One reading saying that your computer’s CPU is at 12% utilization at 12:38:35 UTC on a Tuesday is hard to draw conclusions from.
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It becomes more useful when combined with the rest of the series and visualized.
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This is where trends over time begin to show, and actionable insight can be drawn from the data.
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In addition, time series data is generally written once and rarely updated.
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The result is that InfluxDB is not a full CRUD database but more like a CR-ud, prioritizing the performance of creating and reading data over update and destroy, and [preventing some update and destroy behaviors](/influxdb/v1.5/concepts/insights_tradeoffs/) to make create and read more performant:
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- To update a point, insert one with [the same measurement, tag set, and timestamp](/influxdb/v1.5/troubleshooting/frequently-asked-questions/#how-does-influxdb-handle-duplicate-points).
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- You can [drop or delete a series](/influxdb/v1.5/query_language/database_management/#drop-series-from-the-index-with-drop-series),
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but not individual points based on field values. As a workaround, you can search
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for the field value, retrieve the time, then [DELETE based on the `time` field](/influxdb/v1.5/query_language/database_management/#delete-series-with-delete).
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- You can't update or rename tags yet - see GitHub issue [#4157](https://github.com/influxdata/influxdb/issues/4157)
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for more information. To modify the tag of a series of points, find the points
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with the offending tag value, change the value to the desired one, write the
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points back, then drop the series with the old tag value.
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- You can't delete tags by tag key (as opposed to value) - see GitHub issue
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[#8604](https://github.com/influxdata/influxdb/issues/8604).
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