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Data Control Entitlements allow you to set up permissions for who can view, edit, or approve data within a table. Note that this was formerly called "Design Controls"
Data Entitlements define who has access to do what on your Cinchy platform. These access controls are universally set at a cellular level, meaning that you can configure user access in the way that best supports your use case.
You can set entitlements such that specific users can view, edit, delete, or insert data you want them to access.
Cinchy supports user-based, role-based, and attribute-based access controls.
User-based controls are entitlements given to specific users. This is done via the Users column.
Defining access based on a user means that even if the user changes their role, team, group, etc., they will still maintain their data entitlements.
Role-based controls are entitlements given to set(s) or users based on their role in your environment. For example, you are able to define that only the Product team has access to insert records into a Product Roadmap table. Instead of configuring the entitlements user by user, which takes time and can lead to incorrect data when/if employees shift teams, you can configure it such that any user within the Product team automatically maintains the same level of control across the board.
In Cinchy, this is done via the Groups column.
Attribute-based controls are entitlements given to a user(s) based on a defined set of tags. This can include attributes such as their team, their role, their security clearance, their location, etc.
Defining entitlements based on attributes allows you to drill even deeper into the specificity of which users can do what on your tables.
In Cinchy, you can set up an infinite number of attributes based on your specific use case(s). This is done via Row Filters.
For example, if you have an Employee table that contains salary information visible only to certain people, you can configure a Row Filter such that the logged in user MUST have at least one of the following attributes to be able to see it:
The user to whom the salary belongs
Their manager
All VP level executives
The CEO
You are able to add as many attributes into your Row Filter as needed. For example you could only allow a user with the following set of tags to view a row: Located in Toronto, on the Marketing Team, and with a Security Clearance level of 2.
When viewing a table, click on Data Controls > Entitlements from the left navigation menu (Image 1).
Currently both the table creator and anyone in the Cinchy Administrators
group has access to perform any action on any objects. You can give granular entitlements at a Group or a User level, for both viewing and editing access (Image 2).
In the above scenario, John Smith is part of the Developers group. They're able to view all columns via the entitlement to the Developers group, and they're able to edit both the First Name and Last Name column through different entitlements.
Table-level entitlements apply to the entire table.
Approving this entitlement enables users to see and search for the table in the Marketplace/Homepage.
Approving this entitlement enables users to export data from the table via the Manage Data screen (Image 3).
Approving this entitlement enables users to query the data from the table directly in the Query Builder (Image 4).
Approving this entitlement enables users to alter the structure of the table.
This is a builder/administrative function and shouldn't be granted to end users.
Approving this entitlement enables users to change the permissions on a table.
This is a builder/administrative function and shouldn't be granted to end users.
Column-level entitlements apply only to columns.
Approving this entitlement enables users to view all columns within the table.
Note that this applies to any new columns that are added to the table after providing this permission as well.
This is a drop down where you can select the specific columns you want to grant view access to for users.
Approving this entitlement enables users to edit all columns within the table.
Note that this applies to any new columns that are added to the table after providing this permission as well.
Giving a user edit permission will also give them view permission.
This is a drop down where you can select the specific columns you want to grant edit access to for users.
Giving a user edit permission will also give them view permission.
Approving this entitlement enables users to approve all columns within the table. This also allows users to approve Create and Delete requests.
Note that this applies to any new columns that are added to the table after providing this permission as well.
Approve permissions only apply when Change Approvals are enabled.
Giving a user approve permission will also give them view permission.
This is a drop down where you can select the specific columns you want to grant approve access to for users.
Approve permissions only apply when Change Approvals are enabled.
Giving a user approve permission will also give them view permission.
Link columns require both permission to the column within the table and the column in the link column itself.
Row-level entitlements apply to specific rows. Used in conjunction with Column Level entitlements this allows for granular cell level entitlements.
Approving this entitlement enables users to create new rows in the table.
Approving this entitlement enables users to delete rows in the table.
This is a CQL fragment that applies a filter to which rows will be viewable or editable. Think of the column entitlements and the fragment as a SQL statement applied to the table.SELECT {Edit Selected Columns} WHERE {Editable Row Filter}
Most of these examples will be with the editable row filter so it's easy to see the underlying data for comparison. However this can be done for viewable row data as well.
(Image 5)
With the following entitlements (Image 6):
Edit Specific Columns: Age
Editable Row Filter: [Age] > 30
(Image 7)
View Specific Columns: First Name, Last Name
Viewable Row Filter: [End Date] IS NULL OR [End Date] > GetDate()
(Image 8)
View Specific Columns: All
Edit Specific Columns: First Name, Last Name, Age
Viewable Row Filter: [First Name] = 'John'
Editable Row Filter: [First Name] = 'John'
(Image 9)
For the All Users group:
(Image 10)
View All Columns: Check
Edit Selected Columns: First Name, Last Name
Editable Row Filter: [User Account].[Cinchy Id] = CurrentUserId()
To allow a user to edit certain fields of their own data, you will need an association from a user to the [Cinchy].[Users]
table. You can then use the following function to allow edit for that user, where [...]
is the chain of link columns to get to the Users table.
[...].[Cinchy Id] = CurrentUserId()
This page will describe how to attach files to your Cinchy table rows.
You are able to attach files and images to any row in a Cinchy table by creating a linked column that also links to the 'Files' system table of your platform.
If you have access to view the 'Files' table, you can also view every attachment on your system.
Cinchy supports the attaching of any file type.
Navigate to the table where you want to attach your file.
Click Design Table > Columns
Add a new column with the following parameters (Image 1):
Column Name: Cinchy recommends using a straightforward name like "Images and Files"
Data Type: Link
Linked Table: You will link this to the "Cinchy\Files" table
Linked Column: File Name
Advanced Settings: Make sure to select the "Multi-select" checkbox if you want the capability to add more than one file to a row.
Click Save
Navigate back to your table and locate your newly created files column.
You must create the first row before uploading a file.
To attach a file, click on the upload button (located in the top right hand corner of any cell in the column) (Image 2).
From the pop-up window, select Choose Files to pick your file from your machine, then click Submit (Image 3).
Once uploaded, your cell should look like this (Image 4):
To fully delete your attachment from the platform, you will need to navigate to the Files system table and delete the row associated with your attachment.
You must have edit access to the Files table.
Your file will appear crossed out in your original table and can't be opened or downloaded (Image 5).
This page provides an overview on data erasure.
Data erasure allows you to permanently delete data in Cinchy. As the data owner, you can set an erasure policy on your table if you need to delete data for compliance reasons (Image 1).
The actual erasing of data happens during the maintenance window. Please check with your system administrators to confirm when maintenance is scheduled.
Once data is erased, any links pointing to erased data will look like this (Image 2):
The time is counted based on the record's modified time stamp, not the deleted time stamp. This means for change approval records it's the time when the pending delete request was approved and moved to the Recycle Bin, not when the delete request was made.
You can find the Data Controls menu on the left-hand navigation bar of a table.
From here you may select to:
Change your Data Entitlements
This page guides you through creating table in Cinchy.
Navigate to the Cinchy homepage. In the upper left-hand corner, click on Create to get started. (Image 1)
Select either a Standard or a Spatial Table (Image 2), per the descriptions below.
Spatial Table: A spatial table allows you to create geography and geometry column types, as well as geospatial indexes. You won't be able to create partitions on a spatial table.
Standard Table: You can't create geography or geometry columns in a standard table.
You can't convert from one type to another and will have to either recreate your table or link to another table with geospatial columns.
Any existing tables created before installing Cinchy Platform v4.19.0 are standard tables.
Select From Scratch (Image 3).
A new page will open with the Table Info tab (Image 4). Input the following information:
Table Name: This is a mandatory field, and must be unique to the domain you select.
Icon: You can pick an icon and colour to differentiate your table on thee home screen.
Domain: This is a mandatory field. Select the domain that this table will reside under. If you are have administrative privileges, you can also create new domains from this screen.
Description: You can give your table a description, which will be displayed on the homepage.
When you are finished with the Info page, select Columns from the left-hand navigation bar (Image 5). See here for additional information about column types.
A new page will open with the Columns tab (Image 6). Every table in Cinchy must have at least one column. Input the following information:
Column Name: Input a unique column name.
Data Type: You can select from the following:
Text: All data in this column must be input as text
Number: All data in this column must be input numerically
Date: All data in this column must be in date format. The default is yyyy-mm-dd however you can change that.
Yes/No: All data in this column must be in Yes/No format
Calculated: Data is this column is calculated using a CQL expression
Choice: Data entered in this column must be selected from a set of choice answers that you provide
Link: Data in a link column is pulled from elsewhere on Cinchy
Description: Enter a description of your column
Data Security Classification: You can select from Public, Internal, Confidential, or Restricted. Additional options can be created in the [Cinchy].[Data Security Classifications]
table by an Administrator.
Restricted: Restricted data is the most sensitive data, so you would have to treat it extra carefully. If compromised or accessed without authorization, it could lead to criminal charges, massive legal fines, or cause irreparable damage to the company. Examples include intellectual property, proprietary information or data protected by state and federal regulations.
Confidential: Often, access to confidential data requires additional authorization and explanation of why access to the data is needed. Examples of confidential data include social security numbers, credit card details, phone numbers or medical records. Depending on the industry, confidential data is protected by laws like GDPR, HIPAA, CASL and others.
Internal: This type of data is strictly accessible to internal company personnel or employees who are granted access. This might include internal-only memos, business performance, customer surveys or website analytics.
Public: This type of data is freely accessible to all employees and company personnel. It can be freely used, reused, and redistributed without repercussions. An example might be job descriptions, press releases or links to articles.
Currently there is no functionality tied directly to Data Security Classification - the tagging is just for internal auditing purposes. Future security settings will be tied to Data Security Classifications, rather than simply done at a column level.
Advanced Settings: Select any checkboxes that pertain to your column. See here for more information about these parameters.
You may have further mandatory or optional data to input depending on your selected Data Type.
7. Click on Design Controls > Entitlements in the left navigation pane to set your permissions (Image 7). You may set these as granular as you choose. You may also set permissions on a view by view basis. See here for more about data controls and entitlements.
Click Save to finalize your table.
You may return to change the structure of the existing table (such as rename columns, add new columns, change data type) by clicking on the Design Table button on the left-hand navigation (Image 8).
Navigate to the Cinchy homepage. In the upper left-hand corner, click on Create to get started (Image 9).
Select either a Standard or a Spatial Table (Image 10), per the descriptions below.
Spatial Table: A spatial table allows you to create geography and geometry column types, as well as geospatial indexes. You won't be able to create partitions on a spatial table.
Standard Table: You can't create geography or geometry columns in a standard table.
You can't convert from one type to another and will have to either recreate your table or link to another table with geospatial columns.
Any existing tables created before installing Cinchy Platform v4.19.0 are standard tables.
Select Import a CSV (Image 11).
Enter the following information:
Domain: Select the domain that you table will reside under. If you are have administrative privileges, you can also create new domains from this screen.
File: To create the table, you must upload a .csv file.
The column names in your .csv file must not conflict with System Columns.
When creating a table via Import a CSV, some settings will be set by default:
Default Table Name: The name of the file will be used as the name of the table (a number will be appended if there is a duplicate - ex. uploading Teams.csv will create a table named Teams 1, then Team 2 if uploaded again). You can always rename the table after it has been created.
Default Table Icon: The icon defaults to a green paintbrush.
Default Column Types: Columns by default will be created as a text field, with a maximum length of the longest value in the column. If a column has only numeric values in it, it will be created as a numeric column.
To update these settings, navigate to the Design Table tab on the left navigation bar (Image 12).
When you first create a table, a default view called All Data will be created for you, which you can find on the left navigation bar under Manage Data (Image 13).
You can create additional views by clicking on "+Create View" (Image 14).
You may chose to create a view From Scratch or by Copying an Existing view (Image 15).
Select From Scratch.
The Columns tab will open. Create a Name for your View (Image 16).
If you'd like this to become the default view, toggle the default setting to On (Image 16).
4. Select the column(s) that you want to be visible in this view (Image 17). You may rearrange the column order using drag and drop.
5. Click on the Sort tab in the left navigation bar (Image 18).
6. Use this screen to select which columns you'd like to sort your data by, and in which order. You may rearrange the columns using drag and drop (Image 19).
7. Click on the Filter tab in the left navigation bar. Here, you may use query language to focus your view (Image 20).
8. Click on the Permission tab in the left navigation bar. Here, you may set permissions for who can use this view. By default, it's set to All Users (Image 21).
9. Select Save to finalize your view.
Select From Existing.
Select which view you would like to copy (Image 22).
To update any view, including the Add Data view, click on the pencil icon next to the view's name under Manage Data (Image 23).
Once you create a table, it will be added to your bookmarks by default. Other users (or if you un-star the table from your bookmarks) will see it in the Homepage if they have permissions to.
This page guides you through the various column types available on Cinchy,
Cinchy has system columns used to perform various functionality. These columns can't be modified directly by a user.
You can't create a new column with the same name as a system column.
Cinchy Id is a unique identifier assigned automatically to all records within a normal table. The Cinchy Id is associated with the record permanently and is never reassigned even if the record is deleted.
Version and Draft Version are used to track changes and versions.
Any changes made to a record increments the Version
. Draft Version
is always 0.
Any data approval increments Version
and resets Draft Version
to 0. Any proposed changes increments the Draft Version
.
This is a legacy column. It's always blank.
Created By
is a linked column to the [Cinchy].[Users]
table, of the user who created the record.
Created
is the time when the record was created, per the logged-in user's timezone.
Created By
and Created
will be the same for all records with the same Cinchy Id.
Created By
and Created
is based on the first user to make changes on an approved record.
Modified By
is a linked column to the [Cinchy].[Users]
table, of the user who last modified the record.
The last user to modify the record, and when it happened, per the logged-in user's timezone.
The last user to either modify the record (Draft Version
!= 0) or approve the record (Draft Version
= 0). The timestamp for when that version was generated.
If a record is deleted, it will show up in the Recycle Bin.
A deleted record will have Deleted By
and Deleted
filled in, with the timezone set to the logged-in user's.
Deleted By
and Deleted
are based on the user/time when the Delete Request was created, per the logged-in user's timezone., not when it was approved.
Cinchy always has one latest/up to date record at a time. Anytime changes are made to a record, a new version (normal or draft) is created, and the previous version is updated with a Replaced
timestamp.
Any record where Replaced
is empty is the current version of that record.
Each column must have a unique name. They must also not conflict with system columns (even if you aren't using Change Approvals on the table).
Each column has a data security classification. This defaults to blank, and can be set to one of 4 pre-configured settings (Public, Internal, Restricted, Confidential) or additional options can be created in the [Cinchy].[Data Security Classifications]
table by an administrator.
Currently there is no functionality tied directly to Data Security Classification - the tagging is just for internal auditing purposes. Future security settings will be tied to Data Security Classifications, rather than simply done at a column level.
Public: This type of data is accessible to all employees and company personnel. It can be used, reused, and redistributed without repercussions. An example might be job descriptions, press releases or links to articles.
Internal: This type of data is strictly accessible to internal company personnel or employees who are granted access. This might include internal-only memos, business performance, customer surveys or website analytics.
Confidential: Often, access to confidential data requires additional authorization and explanation of why access to the data is needed. Examples of confidential data include social security numbers, credit card details, phone numbers or medical records. Depending on the industry, confidential data is protected by laws like GDPR, HIPAA, CASL and others.
Restricted: Restricted data is the most sensitive data, so you would have to treat it extra carefully. If compromised or accessed without authorization, it could lead to criminal charges, massive legal fines, or cause irreparable damage to the company. Examples include intellectual property, proprietary information or data protected by state and federal regulations.
Each column can optionally have a description. The description is displayed when you hover on the column header in Data Management.
Be careful when editing a GUID, as you can have unintended consequences.
Checked by default. After saving your changes this will add the column to be displayed in the default table (All Data by default). Generally it makes sense to be checked since there should be a view where all columns are displayed.
If you need to hide a column from certain users or groups you can do so in table controls. It's usually best to have a view where all table columns are displayed.
Makes the column a mandatory field. You won't be able to save or alter a record in a state where a mandatory field is blank.
Requires all values in the column to be unique. Adding a new record or modifying a previous record into a state where it's a duplicate of another record will cause an error and can't be saved.
If you need uniqueness across multiple columns instead, you can create an index in Design Table, add those columns and set the index to unique. If it needs to be more complicated, you can also create a calculated column and set that column to unique. For example, First Name doesn't need to be unique, but First Name + Last Name needs to be unique.
Some fields can also be set to multi-select.
For example, the column Players
in [Football].[Teams]
can be a multi-select field since each team will have multiple players.
Checked by default. This allows other tables to use the column as a link/relationship.
You want to pick identifying columns for linking, such as IDs or Name. Generally you want to use unique columns, but in some cases it's a better user experience to pick an almost unique field for readability.
For example, Full name may not be unique, but it's much easier to understand than Employee ID.
Checked by default. Some columns may not make sense for linking but can be useful to display when someone is choosing an option.
Selecting encryption makes no difference to the user experience within the Cinchy platform. The data is displayed in plain text on the UI or via the query APIs.
Text columns have a maximum length, set to 500 by default.
These are equivalent to VARCHAR(n)
data type in SQL.
You can choose from 3 display formats for number - regular, currency, and percentage. You can also decide how many decimal places to display (0 by default). Note that these are both display settings, and won't affect how the number is stored.
These are equivalent to FLOAT(53)
data type in SQL (default, 8-byte field).
Cinchy has several Date column type display format options available:
MMM DD, YYYY (Oct 31, 2016)
YY-MM-DD (16-10-31)
DD-MM-YYYY (31-10-2016)
DD-MMM-YY (31-Oct-16)
Custom Format
The "Default Value" field isn't mandatory and should be left blank (best practice). However, if populated you won't be able to clear the default date value provided to a "blank" data (no date). You will only be able to overwrite it with another date value.
These are equivalent to DATE()
data type in SQL.
You must select a default value of yes (1) or no (0) for yes/no fields.
These are equivalent to bit
data type in SQL.
You can create a choice column (single or multi-select) in Cinchy. In this scenario, you specify all your choices (1 per newline) in the table design. A user will only be able to select from the options provided.
A calculated column uses values from other fields in the record for its evaluation. These columns also have a specified result type, which dictates the format of the calculated output.
Example:
A [Full Name]
column can be calculated as CONCAT([First Name], ' ', [Last Name])
.
These columns are similar to computed columns in SQL databases.
When creating a calculated column, you have two types to choose from: cached and live. This feature is accessible via the Advanced Settings and was a part of the 4.0 version update.
What It Does: Speeds up data retrieval.
How It's Stored: As an actual column based on a CQL formula.
When It Updates: Updates only if the data in the same row changes.
Example:
Changing a name in a single row only triggers a recalculation for that row's "Label" column.
If a cached column relies on a column from another table, changes in the other table's column won't automatically update the cached column. Make sure to account for this when using cached columns that depend on external data.
What It Does: A live calculated column is a non-cached calculated column that provides real-time data.
How It's Stored: As a formula executed on-the-fly during read or query.
When It Updates: Refreshes automatically upon every query or screen refresh.
When to use:
Your calculated column depends on a value from a linked table and you need the latest value from the linked table.
Your table doesn't contain many records.
Example:
A live "Label" column will update instantly if any referenced data changes, affecting all rows and tables.
Live columns consume more system resources.
Using user-defined functions in live calculated columns can cause errors if they reference other live calculated columns. Only use inbuilt functions in live columns if they reference other live columns.
If you created a spatial table, you will have access to the geography and geometry column types. These columns also have the option to be indexed via Index in the advanced settings on the column itself.
In the UI, this takes a well-known text (WKT) representation of a geometry object. You can modify or paste the WKT representation directly in the editor on the UI. Geometric functions can be performed on this column through CQL and calculated columns.
In the UI, this takes a well-known text (WKT) representation of a geography object. You can modify or paste the WKT representation directly in the editor on the UI. Geographic functions can be performed on this column through CQL and calculated columns.
Hierarchy columns are link columns referencing the current table. For example, the below table contains a list of documentation pages, some of which also have sub-level pages (or even sub-sub-level pages). Using a Hierarchy Column shows the relationships between data.
Example 1: API Overview is the parent page. It had four sub-pages: API Authentication, API Saved Queries, ExecuteCQL, and Webhook Ingestion. Clicking on any of the links within the Sub-Pages column would return you to the row for that specific data set.
Example 2: Builder Guides is the parent page. It has five sub-pages: Best Practices, Creating Tables, Saved Queries, Integration Guides, and CInchy DXD Utility. In this example, we also have another level of hierarchy, wherein Best Practices is also a parent page, and Multilingual Support is its sub-page.
Another common use of Hierarchy columns are to show Manager/Employee relationships
A GUID is a globally unique identifier, formatted as a 128-bit text string, that represents a unique identification. Every column in Cinchy is automatically assigned one. For more information,see the
See to get more context on how they're used.
See to get more context and tips.
If is enabled, you will see the option of Encrypt for columns. If this is checked, the column will be encrypted within the database. This is useful for hiding sensitive information so that people with access to the database directly don't see these fields.
Link columns allow you to establish inherent relationships with other records in other tables. See for more details.
This page provides an overview of Data Compression.
If you need to manage space within your database, you can set a data compression policy. Currently we allow you to permanently delete versions in the collaboration log. Be aware that the current version of compression is a LOSSY process (data will be permanently deleted). Take that into consideration when configuring a policy.
We recommend you keep more versions rather than less versions. You can think of the above as keep any versions newer than 180 days and keeping the most recent 50 versions. As long as a version satisfies one of the two keep conditions, we keep it. Using the example above:
A version that’s from 205 days ago but is amongst the most recent 50 versions (For example: version 22 of 60) will be kept, because it satisfies at least one condition of being in the most recent 50 versions.
A version that’s from 163 days ago but is version 65 of 80 will be kept, because it satisfies at least one condition of being less than 180 days old.
A version that’s from 185 days ago and is version 65 of 80 will be deleted because, it doesn’t satisfy either of the conditions.
The actual compression of data happens during the maintenance window. Please check with your system administrators to confirm when maintenance is scheduled.
The number of versions is based on the major version and not the minor version. This means for a record on version 35.63 with a keep most recent 10 versions, it will keep all versions 26.0 +, rather than all versions 35.44+.
You can apply conditional formatting rules. Our first iteration of this is done directly in the Formatting Rules table. A future iteration will add a UI within a table to create them.
This section has formatting rules available for columns.
This follows the same syntax as a view filter query.
Order in which the formatting rules will apply on the same table. Ordinal 1 will show up above ordinal 2.
Color to highlight the cell. If you want to add your own colors you can do so within System Colours and check off highlight under usage.
Table in which to apply the conditional formatting.
Columns to apply the conditional formatting rules to. You don't need to include any row condition columns within the highlight columns.
A GUID is a globally unique identifier, formatted as a 128-bit text string, that represents a unique identification. Both Cinchy Tables and Columns have a GUID.
This feature is particularly useful when deploying between Cinchy instances.
For example, in a model deployment, you must have matching GUIDs on your columns in order for them to properly load between environment A and environment B. There might be times when these GUIDs don’t automatically match, however, such as if you manually added a new column to environment B and also manually added it to environment A.
In this case, the two columns would have different GUIDs, and the model deployment would fail. With this new feature, however, you can match up conflicting GUIDs to properly load your model.
You have the ability to display and edit these table and column GUIDs within the Design Table screen.
Table GUIDs musts be unique to your specified environment.
Column GUIDs must be unique to the table.
Table GUIDs can be found under Design Table > Info > GUID (Image 1).
Click on the pencil icon to edit the GUID.
GUIDs must adhere to supported formats for Cinchy.
32 hexadecimal (base-16) digits.
Displayed in five groups, separated by hyphens.
The groups take the form of 8-4-4-4-12 characters, for a total of 36 characters (32 hexadecimal characters and 4 hyphens).
Example: 123e4567-e89b-12d3-a456-426614174000
Warning: Changing the value may have damaging effects, proceed with caution.
3. Column GUIDs can be found under Design Table > Columns > GUID (Image 2).
4. Click on the pencil icon to edit the GUID.
GUIDs must adhere to supported formats for Cinchy.
32 hexadecimal (base-16) digits.
Displayed in five groups, separated by hyphens.
The groups take the form of 8-4-4-4-12 characters, for a total of 36 characters (32 hexadecimal characters and 4 hyphens).
Example: 123e4567-e89b-12d3-a456-426614174000
Warning: Changing the value may have damaging effects, proceed with caution.
This page outlines the indexing and partitioning options on your tables.
Use indexing to improve query performance on frequently searched columns within large data sets. Without an index, Cinchy begins a data search with the first row of a table and then follows through the entire table sequentially to find all relevant rows. The larger the table(s), the slower the search.
If the table you are searching for has an index for its column(s), however, Cinchy is able to search much quicker.
In the below example, we will set up a query for a Full Name field. When you create an index for that field, an indexed version of your table is created that's sorted sequentially/alphabetically.
When you run your query on this index, that table will be searched using a binary search.
A binary search won't start from the top record. It will check the middle record with your search criteria for a match. If a match it not found, it will check whether the found value is larger or smaller than the desired value. If smaller, it reruns the data check with the top half of the data, finding the median record. If larger, it reruns the data check with the bottom half of the data, finding the median record. It will repeat until your data is found.
This example uses a table with employee names (Image 1). We want to search for John Smith, using the Full Name column.
To set up your index, select Design Table from the left navigation tab.
Click Indexes (Image 2).
Select "Click Here to Add" and fill out the following information for a new index. Click save when done (Image 3):
Index Name.
Select the column(s) to add to your index. In this example, you want to select the Full Name column to be indexed.
You can select more than one column per index.
Select the Included column(s) to add to your index, if applicable.
The difference between regular columns and Included columns is that indexes with included columns provide the greatest benefit when covering your query because you can include all columns your query may reference, such as columns with data types, numbers, or sizes not allowed as index key columns.
For more on Included Columns, click here
You can now query the full name column for John Smith and receive the results quicker than if you hadn't set up the index (Image 4).
Note that there is no UI change in the query builder or your results when running a query on an indexed column. The difference will be in the speed of your returned results.
A full-text index is a special index type that provides index access for full-text queries against character or binary column data. A full-text index breaks the column into tokens and these tokens make up the index data.
Click on Design Table > Full-text Index
Add in the desired column(s) and click save when done (Image 5).
Columnar Indexing (also known as Columnstore indexing) is available when running SQL Server 2016+. It's not currently available on a PostgreSQL deployment of the Cinchy platform.
Columnar indexes are used for storing and querying large tables. This index uses column-based data storage and query processing to improve query performance. Instead of rowstore or b-tree indexes where the data is logically and physically organized and stored as a table with rows and column, the data in a columnstore indexes is physically stored in columns and logically organized in rows and columns.
You may want to use a columnar index when:
Your table has over 1 million records.
Your data is rarely modified. Having large numbers of deletes can cause fragmentation, which adversely affect compression rates. Updates are also processed as deletes followed by inserts, which will adversely affect the performance of your loading process.
Click on Design Table > Columnar Index
Add in the desired column(s) and click save when done (Image 6).
When using a Columnar Index, you won't be able to add any new columns to your table. You will need to delete the index, add your column(s), and then re-add the index.
Partitioning data in a table is essentially organizing and dividing it into units that can then be spread across more than one file in a database. The benefits of this are:
Improved efficiency of accessing and transferring data while maintaining its integrity.
Maintenance operations can be performed on one or more partitions more efficiently.
Query performance is improved based on the types of queries most frequently run.
When creating a partition in Cinchy, you use the values of a specified column to map the rows of a table into partitions.
This example sets up a partition that divides the employees based on a Years Active column (Image 7). You want to divide the data into two groups: those who have been active for two years or more, and those who have only been active for one year.
Click on Design Table > Partition
Fill in the following information and click save when done (Image 8):
Partitioning Column: this is the column value that will be used to map your rows. This example uses the Years Active column.
Type: Select either Range Left (which means that your boundary will be <=) or Range Right (where you boundary is only <). In this example we want our boundary to be Range Left.
Add Boundary: Add in your boundary value(s). Click the + key to add it to your boundary list. In this example we want to set our boundary to 2.
Once set up, this partition will organize the data into two groups, based on our boundary of those who have a Years Active value of two or above.
You can now run a query on your partitioned table (Image 9).
Note that there is no UI change in the query builder or your results when running a query on a partitioned table. The difference will be in the speed of your returned results.
For more formation on creating, modifying or managing Partitioning, please visit Microsoft's Partitioned table and Indexes documentation.
When you create a column within Cinchy, you can choose to create a link column. A link column allows you to establish inherent relationships with other tables.
Linking is done by the Cinchy ID, which is unique. When you create a link column, you select a column to link to. This is simply a decision on which field to show from the linked record. You should pick a unique field to link on to avoid confusion if possible.
Once a record is created, its Cinchy ID never changes. This means that modifying the row of data in the linked table won't change the relationship in your table to that row. This also means that if you didn't use a unique column, even though the UI looks the same, you are actually linking to different rows.
In general, you should only use unique columns as the linked column. This needs to be balanced with readability in other tables.
For example, Full Name might not be unique to every employee, but it's more readable and understandable than Employee ID. In other cases, it makes sense to link via an ID and add a display column to show relevant information.
To help other builders follow best practices of only linking to unique (or close to unique, such as Full Name) columns, you should un-check the Allow Linking checkbox for non-unique columns so they won't be able to use it for linking.
If this option is unchecked, it prevents users from showing this column in another table.
For example, if you have an ID card # within an employees table, you may not want to display it to the rest of the company because it simply would not be relevant when they're linking to employees and want to see additional information (such as department, title, location). Arguably, a lot of these columns are also taken care of by access controls (since most people won't have access to view that column).
Deselecting this box should be done sparingly, as it doesn't impact the security of your data, only how convenient it's to see it.
When you select a record to link to on the Manage Data screen, it can be useful to see additional information about the records to ensure that it's the record you want to link to (Image 1). You can add additional display columns in the advanced options for link columns (Image 2).
When you type in the cell, all displayed columns will be searched through, not just the Linked Column (Image 3). (Green doesn't have a B in it, but #00B050 does so the Green record shows up)
The link filter filters out records from the drop down list. This is useful for reducing the options to only the typical use case. Commonly used for filtering the drop down to a list of active users or other resources, while not preventing someone from entering missing records with inactive resources.
This is only a display filter; it doesn't prevent other values from being entered as long as they're valid records in the linked table.
You can define 1 to 1, 1 to many, and many to many relationships.
Generally it's rare to link 1:1 relationships since they should usually be in the same table. For example, you would not have a separate table for Employee Phone Number and Employee Address, they would simply be two columns within the Employees table. However there are cases nonetheless where it makes sense, for example, a Keycard tracking table where each keycard has 1 assigned employee.
To enforce a 1:1 relationship within Cinchy, you set the unique constraint and leave it as single-select when creating a link column.
A common relationship to have is a one to many relationship. For example, one customer can have multiple invoices.
To enforce a 1:many relationship within Cinchy, you want to create a link column in the table on the “many” side of the relationship (in the above example, in the invoices table) and leave the link column as single select.
You can also have a many to many relationship. For example, you can have multiple customers, and multiple products. Each customer can order multiple products, and each product can be ordered by multiple customers. Another example is books and authors. An author can write multiple books, but a book can also have multiple authors. You can express many to many relationships in two ways.
For the use case of multiple customers and multiple products, you can use orders as an intermediary table to create indirect relationships between customers and products. Each order has one customer, and each order has multiple products in it. You can derive the relationship between customers and products through the orders table.
To create a many:many relationship through a different entity, you want to create a table for orders. Within orders, you want to create a single-select link to customers and a multi-select link to products.
For the use case of books and authors, it makes sense to create a multi-select link column in the Books table where multiple authors can be selected.
To create a multi-select link column in Cinchy, you select the Multi-Select option when you create a new link column.
System tables are included out-of-the-box with your Cinchy platform, and can be used to track and manage a variety of data.
You can easily query for a list of your system tables using the below:
The system tables included are:
Applets: This system table manages a list of all your integrated applications
Data Experience Definitions: This is a system table for managing data experience definitions
Data Experience References: This is a system table for managing reference data for data experiences
Data Experience Release Artifacts: This is a system table for maintaining data experience release artifacts
Data Experience Releases: This is a system table for maintaining data experience releases
Data Security Classifications: This is a system table for maintaining data security classifications
Data Sync Configurations: This system table manages a list of all your data sync configurations.
Domains: This system table manages a list of all the domains in your instance.
Execution Log: This system table tracks the execution logs of data syncs
Formatting Rules: This system table manages your formatting rules
Groups: System table for managing all groups
Literal Groups: This system table maintains a list of groups
Literal Translations: This system table maintains a list of literal translations
Literals: This system table maintains a list of literals
Regions: This system table maintains a list of regions
Saved Queries: This system table manages a list of all user saved queries that can be exposed via the REST API
System Colours: System table for maintaining colours
Table Access Control: This system table maintains a list of all table access controls in your instance
Table Columns: This system table manages a list of all the system column definitions
Tables: This system table manages a list of all the tables in your instance.
User Defined Functions: This system table manages a list of all your user defined functions
Users: System table for managing all user information including enabling/disabling the ability to create tables, queries, etc.
Views: This system table manages a list of all the views in your instance.