Introduction
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We refer to "document" as a catch all for database record, Web page, PDF/office document, XML document etc. The figures below are for some "average" document across all those types (say 5KB in size) ... if most documents ingested are smaller (eg DB records) then the capacity/performance will be higher and conversely if most documents are larger (eg complex pdf reports) then the capacity/performance will be lower. In practice as you ingest data you should track disk usage against document size to get a more accurate picture of your own data (or just add lots more disk space than could possibly be needed and then monitor performance to decide when to scale). |
Demo configuration
For running in a VM on a laptop to demonstrate the tool. May become slow for more than 100-1000 documents.
Infinit.e API + DB Node | |
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Processor | 1x 1.8+ GHz CPU |
Memory | 1 or 2 GB RAM (swap required to get up to ~8GB total) |
Network | WAN connection/none |
Storage | 20GB |
Compact configuration
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Infinit.e API + DB Node | |
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Processor | 1 X Dual/Quad Core 1.8+ GHz CPUs |
Memory | 4-8 GB RAM (swap required to get up to ~8GB total) |
Network | 1x GigE LAN connection |
Storage | 10 GB Root/OS partition + |
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Infinit.e API Node | Infinit.e Database Node | |
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Processor | 1-2 X Dual Core 1.8+ GHz CPUs | 1-2 X Dual Core 1.8+ GHz CPUs |
Memory | 8-16 GB RAM (or more) | 8-16 GB RAM (or more) |
Network | 2x GigE LAN connection | 2x GigE LAN connection |
Storage | 15 GB Root/OS partition + (~10GB per 1 million "average" documents) | 15 GB Root/OS partition + (~60GB per 1 million "average" documents) |
Operational configuration
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Infinit.e API Node | Infinit.e Database Node | |
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Processor | 2 X Dual Core 1.8+ GHz CPUs | 2 X Dual Core 1.8+ GHz CPUs |
Memory | 16 GB RAM or more (32GB is ideal) | 16 GB RAM or more (32GB is ideal) |
Network | 2x GigE LAN connection | 2x GigE LAN connection |
Storage | 20 GB Root/OS partition + | 20 GB Root/OS partition + 600+ GB data partition, RAID-0 (~60GB per 1 million "average" documents) |
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Note the DB scales per 2-node block, since the primary benefit of the second node is redundancy rather than performance - although it balances the reads somewhat (not the writes) so there is some (not 2x) performance gain within a replica set. |
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