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Version: Latest (4.48.0)

System Requirements

OS

  • Windows 10 or higher
  • Windows server 2012 or higher

Memory

  • 8 GB RAM minimum
  • 16 GB RAM recommended

Processor

  • Intel Core i7-960 Processor 3.20 GHz 8 MB or equivalent
  • Intel Xeon E3 1270 V6 or equivalent

Software

  • MongoDB Server version 5.x or higher
  • A browser, preferred latest version of Firefox or Chrome

Other Requirements

We recommend whitelisting the following IPs on port 443, which are used by our license validation platform

IPNotes
23.102.21.212app.cryptolens.io, used at the moment
40.113.70.59api.cryptolens.io, will be used in newer updates
20.82.170.150api.cryptolens.io, reserved and may be used in the future

If you need to run CPU intensive flows it is recommended to run Mongo on a different machine.

Drive configuration

  • The best practice drive configuration is: one drive for the system, one drive for the software (minimum of 10GB) and one drive for the migration data (which is preferably an SSD)
  • For the minimum size of the data drive, use the following formula: S = TS * 2,25
  • Where TS is the total size of the data to migrate in TB and S is the size of the data drive in GB

The data drive configuration applies to the machine where MongoDB is installed, since it stores all the migration data. However, as a user you can choose to story binary data outside of the Content Store (which uses MongoDB). In that case, the data drive configuration applies to the machine where the binary data is stored.

Best practices

Although it is possible to run Xill4 and MongoDB on the same machine, it is not recommended when performing actual migrations. When performing migrations, a distributed setup with a separate machine for MongoDB is recommended. This will ensure that MongoDB's performance will not be affected by the flows that are being executed by Xill4 or vice versa.

For deploying MongoDB, we refer to their recommended system requirements. In addition, a minimum of 32GB of RAM is recommended, which is suitable for most data sets.