Import CSV Files

As of R22.0.48.00, this method of CSV Import replaces the old method.

Overview 

 For example perhaps you want to view some lab sample results that are stored in a text file, but you want to see the data on trends next to your other process data from a historian. This article provides step-by-step instructions to get your text CSV data into Seeq for further analysis.

Note

Seeq does not keep a copy of the CSV file provided. We recommend that users keep copies of their files for future reference.

Datafile

A CSV import creates a Datafile which contains the signals/condition that result from the import of that CSV file.

Import Requirements

  • CSV Import has the following file size limits. If your files are larger, split them up or ask your Seeq admin to increase the CSV limits which can be found in the Configuration System. The limits are in place to keep large files from swamping the system. Seeq does not test files larger than these limits. Increase these limits at your own risk.

    • 500 MB

    • 1000 columns

    • 1.1 million rows

  • A file can be used for signal(s) import or condition import but not both at the same time.

  • Signals:

    • One key column per file per import. If the file has more than one key column possibility, import that file again and choose a different key column.

    • Many signals can be imported at once. Each column of the file can be mapped to a signal.

    • Within a signal, there can be only one sample per key. If there are more than one sample per key, the most recently uploaded sample will replace any sample uploaded earlier.

  • Conditions:

    • One condition per import (in other words, one set of start, end columns per import). If you'd like to create multiple conditions from one file, import the file again choosing different columns.

    • Within a condition, there can only be one capsule with identical start times, end times, and properties. However, there can be more than one capsule with identical start and end times if the properties differ between those capsules. Keep this in mind when appending additional capsules to an existing condition as it could impact whether capsules are overwritten or remain.

  • Header Row + Data Row Start

    • A header row with unique column names is required. The uniqueness test is case insensitive. For example, "Value" and "value" are not considered unique.

    • The header rows must come before the data rows.

Quotes

Quotes are not interpreted as a special character. A CSV row of 2020-10-04T00:00Z, 1, 2, "1,234", 5 will be interpreted as these entries:

  • 2020-10-04T00:00Z

  • 1

  • 2

  • "1

  • 234"

  • 5

Supported Delimiters

Comma, Semicolon, and Tab delimiters are supported.

  • R22.0.48+: Comma is the default but the Import UI provides the opportunity to change the delimiter.

  • R22.0.49+: The factory default is comma however, the default for each Seeq server can be configured. The Import UI still provides the opportunity to change the delimiter regardless of the default configuration.

Supported Decimal Formats

  • R22.0.48+: Only period as decimal is supported.

  • R22.0.49+: Comma and period as decimal are both supported. If using comma for the decimal, comma cannot be used as the delimiter. Be sure to change the delimiter import setting appropriately.

Supported Timestamp Formats

  • The same format must be used for the entirety of the key column.

  • If the timestamp includes time zone information, it will be used.

  • If the timestamp does not include time zone information, the time zone specified in the import UI will be used. If not specified in the UI, the Seeq server time zone will be used.

  • Starting in R22.0.49, day first dates are supported in addition to the already supported month first dates. This creates the potential for ambiguous dates.

Ambiguous Dates (R22.0.49+)

07/04/2019 could be July 4, 2019 or April 7, 2019.

If a non-ambiguous date such as 13/4/2019 or 4/13/2019 can be found in the first few lines of the file, Seeq will automatically determine whether day-first or month-first dates were intended.

If Seeq cannot automatically determine day-first versus month-first, it will fall back upon the import setting dayFirstDefault. If true, the dates will be interpreted as day first. If false, the dates will be interpreted as month first. The factory default is dayFirstDefault = false however, the default for each Seeq server can be configured. The Import UI still provides the opportunity to change this setting regardless of the default configuration.

R22.0.48 Supported Timestamps

Generally (unless stated otherwise)

  • Time not required

  • If providing time, hours and minutes are required but seconds are not required

  • Fractional seconds supported but not required. Nanoseconds are the smallest supported time unit.

ISO 8601:

  • date and time required

  • 4 digit year, 2 digit month and day required

  • supported year month day separator:  dash

  • supported date and time separators: T, space

Examples: ISO 8601
1 2 3 4 5 6 7 8 2016-01-08 22:52 2016-01-08T22:52:12Z 2016-01-08T22:52:12.123456789 2016-01-08 22:52:12.123456789Z 2016-01-08T22:52:12.123456789+00:00 2016-01-08T22:52:12.123456789 UTC 2016-01-08T14:52:12.123456789-08:00 2016-01-08T14:52:12.123456789 America/Los_Angeles

Month Day Year

  • 2 or 4 digit year supported

  • 1 or 2 digit month and day supported

  • supported month day year separator: forward slash

  • If 12 hr clock, AM and PM are required and must be capitalized

Examples: Month Day Year 24Hr Clock
1 2 3 4 5 6 1/8/19 22:52 1/08/19 22:52:12 01/8/19 22:52:12.12345 01/08/2019 22:52:12.123456789 7/30/08 3:42 7/30/2008 03:42
Examples: Month Day Year 12Hr Clock
1 2 3 4 5 6 1/8/19 10:52 PM 1/08/19 10:52:12 PM 01/08/19 10:52:12.123 PM 01/08/2019 10:52:12.123456789 PM 7/30/08 3:42 AM  7/30/2008 03:42 AM 

Seconds since Unix Epoch

  • fractional seconds not supported

  • Example: 1452293532

R22.0.49+ Supported Timestamps

In short, R22.0.49 adds support for:

  • fractional seconds since Unix Epoch

  • more timestamp punctuation, capitalization, and spacing flexibility

  • day first dates

R22.0.50 adds support for:

  • time first keys (Example: 17:04 15/03/2020)

Generally (unless stated otherwise):

  • 2 digit or 4 digit year supported

  • 1 or 2 digit month, day, hour supported

  • Only 2 digit minutes and seconds supported

  • Time not required

  • If providing time, hours and minutes are required but seconds are not required

  • Fractional seconds supported but not required. Nanoseconds are the smallest supported time unit.

  • date first keys support 12 or 24 hour clock

    • For 12 hr clocks, AM and PM are required but do not have to be capitalized

  • time first keys support 24 hour clock by not 12 hour clock

  • Supported separators: 

    • One of many possible examples to illustrate: 2019-04-22T10:12:42.32 

      • date separator (2019-04-22): forward slash, dash, period, period space (The day/month separator does not have to match the month/year separator.)

      • date followed by time separator (T): T, space, period, period space

      • time followed by date separator: space

      • fractional seconds separator (42.32): period, comma, colon

      • hour minute seconds separator (10:12:42): colon

Examples: Date Separator Variations
1 2 3 4 5 6 2019/04/22 2019-04-22 2019.04.22 2019. 04. 22 2019/04.22 etc.
Examples: Variations on the Separator between Date followed by Time
1 2 3 4 5 6 7 8 9 10 11 2019-04-22 2019-04-22T 2019-04-22. 2019. 04. 22. 2019-04-22T10:12 2019-04-22 10:12 2019.04.22.10:12 2019. 04. 22. 10:12 2019.04.22 10:12 2019/04/22.10:12 etc.
Examples: Variations on the Separator between Time followed by Date
1 2 3 14:12 22/4/19 14:12:13.23 04/22/2019 etc.
Examples: Fractional Seconds Separator Variations
1 2 3 2019-04-22T10:12:15.123 2019-04-22T10:12:15,123 2019-04-22T10:12:15:123

Year Month Day (ISO 8601 and similar):

  • 4 digit year required

  • 24 hour clock required

Examples: Year Month Day 24Hr Clock
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2019-1-8 2019.01.8 2019/01/08 2019. 01. 08 2019. 1. 8. 2019/1/8T 2019.1.08. 2019-01-8/. 2019-01-08 22:52 2019-01-08T22:52:12Z 2019-01-08.22:52:12.12 2019-01-08 22:52:12,1234 2019-01-08T22:52:12:123456789Z 2019/01/08T22:52:12.123456789+00:00 2019/01-08T22:52:12.123456789+00:00 2019-01-08 22:52:12.123456789 UTC 2019.01.08.14:52:12.123456789-08:00 2019. 1.08 14:52:12.123456789 America/Los_Angeles 2019. 1. 8. 14:52:12.123456789 America/Los_Angeles 2019. 1.08 14:52:12.123456789 America/Los_Angeles (and many other possible combinations)

Month Day Year or Day Month Year

  • If using a 12 hr clock, AM and PM are required but do not have to be capitalized

Examples: Month Day Year or Day Month Year
1 2 3 4 5 6 7 8 9 10 1/8/19 1/08/2019 01/8/2019 1.8.19. 1-8-19T 1. 08. 2019. 01/8/19T (and many other possible combinations)
Examples: Month Day Year or Day Month Year followed by 12Hr Clock
1 2 3 4 5 6 7 1/8/19 10:52 PM 1-08-19T10:52:12AM 01.8.19.10:52:12.12345 pm 01. 08. 2019. 10:52:12,12345678 am 01/8-19.10:52:12:12345678 Pm (and many other possible combinations)
Examples: Month Day Year or Day Month Year followed by 24Hr Clock
1 2 3 4 5 6 7 8 9 10 11 12 13 1/8/19 22:52 1-08-19T22:52:12 01.8.19.22:52:12.12345 01. 08. 2019. 22:52:12,12345678 01/8-19.22:52:12:12345678 1/8/19 3:52 1-08-19T03:52:12 01.8.19.3:52:12.1 01. 08. 2019. 03:52:12,123 1-8. 2019T03:52:12:123456789 (and many other possible combinations)
Examples: 24Hr Clock followed by Month Day Year or Day Month Year
1 2 3 4 5 6 7 8 9 10 11 12 13 22:52 1/8/19 22:52:12 1-08-19 22:52:12.12345 01.8.19. 22:52:12,12345678 01. 08. 2019. 22:52:12:12345678 01/8-19. 3:52 1/8/19 03:52:12 1-08-19 3:52:12.1 01.8.19. 03:52:12,123 01. 08. 2019. 03:52:12:123456789 1-8. 2019 (and many other possible combinations)

Seconds since Unix Epoch

  • supported fractional seconds separators: period, comma

Examples: Seconds Since Unix Epoch
1 2 3 4 5 6 7 1452293532 1452293532. 1452293532, 1452293532.12 1452293532,12345 1452293532.1234567 1452293532,123456789

Importing a CSV File

The CSV Import interface can be found in the Seeq Workbench under the Tools tab.

Upload the file here and then fill out the information about the condition or signal(s). Hovering over the ? icons will provide documentation about the options. For advanced use cases, explore the Optional Settings.

Replace and Append

Another CSV file can be imported to an already existing Datafile.

Start by searching for the Datafile on the Data tab and then selecting edit.

If the new CSV file matches the original import, the condition or signals created by the original import will be modified. If the CSV file does not match the original import, an additional condition or signals will be created.

Replace: When replace is chosen, all previously imported samples/capsules of the matching signal/condition are deleted regardless of the date range of the data in the CSV file.

Append: When append is chosen, the previously imported samples/capsules remain unless:

  • The new file has samples at the same key as previously existing samples. Only one sample per key is kept. The most recently imported sample for the particular key is the sample that is kept.

  • The new file has capsules with identical start time, end time, and properties as a previously imported capsule. Only one identical capsule is kept. The most recently imported capsule is the capsule that is kept.

When appending or replacing data, the Datafile defaults to the import settings that were used previously to save the user time spent configuring import. These settings are just the default and can be changed as needed.

Modifying existing CSV conditions

When importing a file to an already existing Datafile, a condition created by the original import will be modified if the condition name specified in the import tool matches the name of the condition from the original import.

Modifying existing CSV signals

When importing a file to an already existing Datafile, signals created by the original import will be modified if the CSV file has column names that match the original import. The column name and file that the signal was imported from is logged in the item's description. Looking at the description can help plan signal modification.

However, in most cases, the signal name is named after the column making it easy to plan modifications.

The situations where the signal name differs from the column name are:

  • A name suffix and prefix were used during import in which case the signal name is "PrefixColumnNameSuffix".

  • The signal was imported using CSV V1 and then migrated in the upgrade to R22.0.48.00+. In this case, the signal name is very likely different than the column name.

Daylight Savings Tips

In the following examples, the 2016 daylight savings transitions in region America/Los_Angeles will be used as example.

Spring forward:

When local standard time was about to reach
Sunday, March 13, 2016,2:00:00 am clocks were turned forward 1 hour to
Sunday, March 13, 2016,3:00:00 am local daylight time instead
which means that the 2:00 am hour doesn't exist in local time.

Fall back:

When local daylight time was about to reach
Sunday, November 6, 2016,2:00:00 am clocks were turned backward 1 hour to
Sunday, November 6, 2016,1:00:00 am local standard time instead
which means that there are two 1:00 am hours in local time.



The best practice is to log CSV data in UTC time in which case there are no daylight savings headaches because there are no missing or repeated hours.

Los Angeles daylight savings transitions logged in UTC time
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 MyTime,MyValue 2016-03-13T08:00:00Z,7.0 2016-03-13T08:15:00Z,8.0 2016-03-13T08:30:00Z,5.0 2016-03-13T08:45:00Z,1.0 2016-03-13T09:00:00Z,3.0 2016-03-13T09:15:00Z,2.0 2016-03-13T09:30:00Z,6.0 2016-03-13T09:45:00Z,0.0 2016-03-13T10:00:00Z,4.0 2016-03-13T10:15:00Z,2.0 2016-03-13T10:30:00Z,4.0 2016-03-13T10:45:00Z,1.0 2016-03-13T11:00:00Z,7.0 2016-03-13T11:15:00Z,1.0 2016-03-13T11:30:00Z,0.0 2016-03-13T11:45:00Z,3.0 2016-11-06T07:00:00Z,7.0 2016-11-06T07:15:00Z,8.0 2016-11-06T07:30:00Z,5.0 2016-11-06T07:45:00Z,1.0 2016-11-06T08:00:00Z,3.0 2016-11-06T08:15:00Z,2.0 2016-11-06T08:30:00Z,6.0 2016-11-06T08:45:00Z,0.0 2016-11-06T09:00:00Z,5.0 2016-11-06T09:15:00Z,4.0 2016-11-06T09:30:00Z,8.0 2016-11-06T09:45:00Z,2.0 2016-11-06T10:00:00Z,9.0 2016-11-06T10:15:00Z,4.0 2016-11-06T10:30:00Z,8.0 2016-11-06T10:45:00Z,9.0 2016-11-06T11:00:00Z,4.0 2016-11-06T11:15:00Z,2.0 2016-11-06T11:30:00Z,4.0 2016-11-06T11:45:00Z,1.0 2016-11-06T12:00:00Z,7.0 2016-11-06T12:15:00Z,1.0 2016-11-06T12:30:00Z,0.0 2016-11-06T12:45:00Z,3.0

 

The next best practice is to log CSV data in local time but including the offset in the timestamp.

Los Angeles daylight savings transitions logged in local time with offset information
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 MyTime,MyValue 2016-03-13T00:00:00-08:00,7.0 2016-03-13T00:15:00-08:00,8.0 2016-03-13T00:30:00-08:00,5.0 2016-03-13T00:45:00-08:00,1.0 2016-03-13T01:00:00-08:00,3.0 (1:00 am hour) 2016-03-13T01:15:00-08:00,2.0 2016-03-13T01:30:00-08:00,6.0 2016-03-13T01:45:00-08:00,0.0 2016-03-13T03:00:00-07:00,4.0 (3:00 am hour. Note the missing 2:00 am hour. The change in offset prevents confusion.) 2016-03-13T03:15:00-07:00,2.0 2016-03-13T03:30:00-07:00,4.0 2016-03-13T03:45:00-07:00,1.0 2016-03-13T04:00:00-07:00,7.0 2016-03-13T04:15:00-07:00,1.0 2016-03-13T04:30:00-07:00,0.0 2016-03-13T04:45:00-07:00,3.0 2016-11-06T00:00:00-07:00,7.0 2016-11-06T00:15:00-07:00,8.0 2016-11-06T00:30:00-07:00,5.0 2016-11-06T00:45:00-07:00,1.0 2016-11-06T01:00:00-07:00,3.0 (1st 1:00 am hour) 2016-11-06T01:15:00-07:00,2.0 2016-11-06T01:30:00-07:00,6.0 2016-11-06T01:45:00-07:00,0.0 2016-11-06T01:00:00-08:00,5.0 (2nd 1:00 am hour. The change in offset prevents confusion.) 2016-11-06T01:15:00-08:00,4.0 2016-11-06T01:30:00-08:00,8.0 2016-11-06T01:45:00-08:00,2.0 2016-11-06T02:00:00-08:00,9.0 2016-11-06T02:15:00-08:00,4.0 2016-11-06T02:30:00-08:00,8.0 2016-11-06T02:45:00-08:00,9.0 2016-11-06T03:00:00-08:00,4.0 2016-11-06T03:15:00-08:00,2.0 2016-11-06T03:30:00-08:00,4.0 2016-11-06T03:45:00-08:00,1.0 2016-11-06T04:00:00-08:00,7.0 2016-11-06T04:15:00-08:00,1.0 2016-11-06T04:30:00-08:00,0.0 2016-11-06T04:45:00-08:00,3.0

However, it is common for data to be logged in local time but without any offset information in the timestamp. 

Los Angeles daylight savings transitions logged in local time without offset information
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 MyTime,MyValue 2016-03-13T00:00,7.0 2016-03-13T00:15,8.0 2016-03-13T00:30,5.0 2016-03-13T00:45,1.0 2016-03-13T01:00,3.0 (1:00 am hour) 2016-03-13T01:15,2.0 2016-03-13T01:30,6.0 2016-03-13T01:45,0.0 2016-03-13T03:00,4.0 (3:00 am hour. Note the missing 2:00 am hour.) 2016-03-13T03:15,2.0 2016-03-13T03:30,4.0 2016-03-13T03:45,1.0 2016-03-13T04:00,7.0 2016-03-13T04:15,1.0 2016-03-13T04:30,0.0 2016-03-13T04:45,3.0 2016-11-06T00:00,7.0 2016-11-06T00:15,8.0 2016-11-06T00:30,5.0 2016-11-06T00:45,1.0 2016-11-06T01:00,3.0 (1st 1:00 am hour) 2016-11-06T01:15,2.0 2016-11-06T01:30,6.0 2016-11-06T01:45,0.0 2016-11-06T01:00,5.0 (2nd 1:00 am hour. Time appears to have gone backward. This causes confusion. Is this 2nd hour the daylight shift? Or were two samples taken at the same time?) 2016-11-06T01:15,4.0 2016-11-06T01:30,8.0 2016-11-06T01:45,2.0 2016-11-06T02:00,9.0 2016-11-06T02:15,4.0 2016-11-06T02:30,8.0 2016-11-06T02:45,9.0 2016-11-06T03:00,4.0 2016-11-06T03:15,2.0 2016-11-06T03:30,4.0 2016-11-06T03:45,1.0 2016-11-06T04:00,7.0 2016-11-06T04:15,1.0 2016-11-06T04:30,0.0 2016-11-06T04:45,3.0

When the data is logged in local time without timestamp information, the missing spring hour doesn't cause trouble. However, the intention of the repeated hour is ambiguous. Is that the daylight shift? Or were multiple measurements taken at the same timestamp?

Seeq attempts to infer the intention. When importing a CSV file, a time zone is specified. Seeq will check this time zone for its daylight savings fall back transition. If there is a repeated hour that aligns with the fall back transition, Seeq will assume that the 2nd 1:00 hour is a different hour than the 1st 1:00 hour.

There are a couple situations where Seeq is not able to infer the intention.

Situation 1: The first 1:00 am hour is in file 1. The second 1:00 am hour is in file 2. File 2 is imported later and appended to the Datafile. In this case, the 1:00 am samples from File 1 will be overwritten with the 1:00 am samples from File 2.

Situation 2: Time does not appear to go backward. In this case, all 1:00 am samples will be recorded in the same hour.

Los Angeles daylight savings fall back transition is ambiguous
1 2 3 4 5 6 7 8 9 10 11 2016-11-06T00:00,7.0 2016-11-06T00:10,8.0 2016-11-06T00:40,1.0 2016-11-06T01:00,3.0 (1st 1:00 am hour intended) 2016-11-06T01:10,2.0 (1st 1:00 am hour intended) 2016-11-06T01:40,5.0 (2nd 1:00 am hour intended) 2016-11-06T01:55,4.0 (2nd 1:00 am hour intended) 2016-11-06T02:00,9.0 2016-11-06T02:15,4.0 2016-11-06T02:30,8.0 2016-11-06T02:45,9.0

LenientDaylightSavings

The import has a LenientDaylightSavings option which impacts the handling of data logged in the vicinity of the spring forward daylight savings transition.

In the example above, the 2:00 AM hour doesn't exist during spring forward. Data logged properly in local time therefore should show the 2:00 AM hour as missing like the above examples do.  However, we still see customer files with data in this non-existent hour. (How did it get there?) By default, Seeq will fail to import the data if it detects this problem. This gives the user a chance to make sure that they data is actually consistent with the time zone provided.

If the user would like to proceed despite having data in a non-existent hour, the lenientDaylightSavings option can be enabled in which case the data in the non-existent period will be interpreted as being in the period directly after it. For example, in the Los Angeles region, 2:00 AM data would be interpreted as 3:00 AM data. Examine the following datasets to understand the implications of this.

Example 1: Evenly spaced samples.

Because:

  • Samples are evenly spaced.

  • Seeq only keeps one sample per key. It keeps the last sample uploaded.

  • 2:00 data is shifted to 3:00.

The 3:00 hour "overwrites" the 2:00 am hour. The 2:00 am hour is now missing.

Example 1: Evenly spaced samples in Los Angeles daylight savings spring forward
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ORIGINAL FILE MyTime,MyValue 2016-03-13T01:00,1.0 (1:00 am hour) 2016-03-13T01:15,2.0 2016-03-13T01:30,3.0 2016-03-13T01:45,4.0 2016-03-13T02:00,5.0 (2:00 am hour. This hour doesn't exist!) 2016-03-13T02:15,6.0 2016-03-13T02:30,7.0 2016-03-13T02:45,8.0 2016-03-13T03:00,9.0 (3:00 am hour) 2016-03-13T03:15,10.0 2016-03-13T03:30,11.0 2016-03-13T03:45,12.0 2016-03-13T04:00,13.0 DATA IN SEEQ MyTime,MyValue 2016-03-13T01:00,1.0 (1:00 am hour) 2016-03-13T01:15,2.0 2016-03-13T01:30,3.0 2016-03-13T01:45,4.0 2016-03-13T03:00,9.0 (3:00 am hour) 2016-03-13T03:15,10.0 2016-03-13T03:30,11.0 2016-03-13T03:45,12.0 2016-03-13T04:00,13.0

 

Example 2: Unevenly spaced samples.

Because:

  • Samples are unevenly spaced.

  • 2:00 data is shifted to 3:00.

The 2:00 and 3:00 data is now interspersed.

Example 2: Unevenly spaced samples in Los Angeles daylight savings spring forward
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 ORIGINAL FILE MyTime,MyValue 2016-03-13T01:00,1.0 (1:00 am hour) 2016-03-13T01:15,2.0 2016-03-13T01:30,3.0 2016-03-13T01:45,4.0 2016-03-13T02:05,5.0 (2:00 am hour. This hour doesn't exist!) 2016-03-13T02:20,6.0 2016-03-13T02:40,7.0 2016-03-13T02:50,8.0 2016-03-13T03:00,9.0 (3:00 am hour) 2016-03-13T03:15,10.0 2016-03-13T03:30,11.0 2016-03-13T03:45,12.0 2016-03-13T04:00,13.0 DATA IN SEEQ MyTime,MyValue 2016-03-13T01:00,1.0 (1:00 am hour) 2016-03-13T01:15,2.0 2016-03-13T01:30,3.0 2016-03-13T01:45,4.0 2016-03-13T03:00,9.0 (3:00 am hour) 2016-03-13T03:05,5.0 2016-03-13T03:15,10.0 2016-03-13T03:20,6.0 2016-03-13T03:30,11.0 2016-03-13T03:40,7.0 2016-03-13T03:45,12.0 2016-03-13T03:50,8.0 2016-03-13T04:00,13.0

Configuring Server Defaults (R22.0.49+)

Import preferences that tend to vary by region can be configured for a Seeq server. Those settings can be found in the Configuration section of the Administration panel. For example, US servers may prefer the factory defaults of DayFirstDefault = false and DelimiterDefault = comma. German servers may prefer DayFirstDefault = true and DelimiterDefault = semicolon. Seeq does not need to be restarted for the changes to take effect. However, the browser may need to be reloaded to see the change in defaults.



Security considerations

Seeq will import the contents of the CSV files but keep no permanent copies of the file. If the file is not readable as a CSV file, it is rejected (thus protecting against uploading binary/executable files).

By default, data from CSV files is scoped to the Workbook and inherits the permissions of the Workbook.