From bf83761aa15fe25697c6f136a9075d8bec7a49b6 Mon Sep 17 00:00:00 2001 From: Priyanka Danesh Gunaki Date: Tue, 4 Aug 2026 12:41:09 +0530 Subject: [PATCH 1/4] DOC-1153 Add Datasize Estimates --- .../pages/hw-and-sw-requirements.adoc | 115 ++++++++++++++++++ 1 file changed, 115 insertions(+) diff --git a/modules/installation/pages/hw-and-sw-requirements.adoc b/modules/installation/pages/hw-and-sw-requirements.adoc index a2349b0c..34a6fdb5 100644 --- a/modules/installation/pages/hw-and-sw-requirements.adoc +++ b/modules/installation/pages/hw-and-sw-requirements.adoc @@ -65,6 +65,7 @@ The software has been tested on the operating systems listed below: | Windows (all version) | X + | MacOS (Intel and M1 chip) | X |=== @@ -211,6 +212,120 @@ NOTE: Production systems should consider having replica clusters for redundancy | 10 Gigabit Ethernet adapter |=== +=== Hardware Sizing Estimates by Dataset Size + +The estimates in this section provide general guidance for sizing hardware based on dataset size, workload type, and compression ratio. They are intended to help with initial capacity planning before selecting hardware for a TigerGraph deployment. + +Algorithmic workloads, such as PageRank, Community Detection, and Shortest Path, generally require more memory than non-algorithmic workloads because they perform additional graph computations. + +==== Example + + + +The following example demonstrates how workload type and compression ratio affect hardware sizing. + +Assumptions: + +* Raw dataset size: **500 GB** +* High Availability deployment +* Algorithmic workload +* CPU recommendation: Up to 64 cores + +Using the sizing methodology for algorithmic workloads: + +* At a 10% compression ratio: +** Memory: **1.35 TB** +** Disk per node: **~2 TB** + +* At a 40% compression ratio: +** Memory: **900 GB** +** Disk per node: **~1.35 TB** + +This example illustrates that the same raw dataset may require different hardware depending on the achieved compression ratio. + +.Estimated hardware requirements by dataset size + +[cols="1,1,1,1,1,1",options="header"] +|=== +|Raw Data Size +|Compression Ratio +|CPU Recommendation +|Memory (Algorithmic) +|Memory (Non-Algorithmic) +|Disk per Node + +|100 GB +|10% +|Up to 64 cores +|270 GB +|180 GB +|~405 GB + +|100 GB +|40% +|Up to 64 cores +|180 GB +|120 GB +|~270 GB + +|500 GB +|10% +|Up to 64 cores +|1.35 TB +|900 GB +|~2 TB + +|500 GB +|40% +|Up to 64 cores +|900 GB +|600 GB +|~1.35 TB + +|1 TB +|10% +|Up to 64 cores +|2.7 TB +|1.8 TB +|~4.05 TB + +|1 TB +|40% +|Up to 64 cores +|1.8 TB +|1.2 TB +|~2.7 TB + +|5 TB +|10% +|Up to 64 cores +|13.5 TB +|9 TB +|~20.25 TB + +|5 TB +|40% +|Up to 64 cores +|9 TB +|6 TB +|~13.5 TB + +|10 TB +|10% +|Up to 64 cores +|27 TB +|18 TB +|~40.5 TB + +|10 TB +|40% +|Up to 64 cores +|18 TB +|12 TB +|~27 TB +|=== + +NOTE: These estimates are intended for initial capacity planning only. Actual hardware requirements vary depending on graph schema, workload characteristics, compression ratio, query complexity, replication factor, partitioning strategy, and performance objectives. For production deployments, consult TigerGraph Solution Architects to determine the appropriate hardware configuration. === Additional Considerations This section describes considerations beyond sizing for the CPU, memory, and storage. From a0020b34826aca13adfe74f5bd837f9ba5f92a34 Mon Sep 17 00:00:00 2001 From: priyankagunaki-cloud Date: Wed, 5 Aug 2026 14:59:58 +0530 Subject: [PATCH 2/4] DOC-1153 Update hw-and-sw-requirements.adoc --- .../pages/hw-and-sw-requirements.adoc | 42 +++++++++++++++---- 1 file changed, 33 insertions(+), 9 deletions(-) diff --git a/modules/installation/pages/hw-and-sw-requirements.adoc b/modules/installation/pages/hw-and-sw-requirements.adoc index 34a6fdb5..94969580 100644 --- a/modules/installation/pages/hw-and-sw-requirements.adoc +++ b/modules/installation/pages/hw-and-sw-requirements.adoc @@ -218,30 +218,54 @@ The estimates in this section provide general guidance for sizing hardware based Algorithmic workloads, such as PageRank, Community Detection, and Shortest Path, generally require more memory than non-algorithmic workloads because they perform additional graph computations. -==== Example - +Compression ratio also affects memory and storage requirements. For example, a 10% compression ratio means the stored graph occupies approximately 90% of its original size, while a 40% compression ratio means it occupies approximately 60% of its original size. +==== Example The following example demonstrates how workload type and compression ratio affect hardware sizing. Assumptions: -* Raw dataset size: **500 GB** -* High Availability deployment +* Raw dataset size: 500 GB +* 3-node HA cluster +* Replication factor: 3 * Algorithmic workload * CPU recommendation: Up to 64 cores +The memory and disk estimates in this example are calculated per node. + Using the sizing methodology for algorithmic workloads: * At a 10% compression ratio: -** Memory: **1.35 TB** -** Disk per node: **~2 TB** +** Memory: 1.35 TB +** Disk per node: ~2 TB * At a 40% compression ratio: -** Memory: **900 GB** -** Disk per node: **~1.35 TB** +** Memory: 900 GB +** Disk per node: ~1.35 TB + +==== Memory and Disk Sizing + +Memory recommendations are based on the following workload-specific sizing factors. + +[cols="2,2",options="header"] +|=== +|Workload type +|Memory sizing factor + +|Non-algorithmic +|2 (1x for graph size + 1x for compute) + +|Algorithmic +|3 (1x for graph size + 2x for compute) +|=== + +Disk recommendations: + +* Disk space per node should be approximately equal to the recommended memory per node. +* If the deployment stores multiple backups, large data files, or exported data locally, provision approximately 2× the recommended memory size for disk capacity. -This example illustrates that the same raw dataset may require different hardware depending on the achieved compression ratio. +The following estimates represent recommended resources per node for the assumed cluster configuration. .Estimated hardware requirements by dataset size From 01d2e8b5e4cf270265d2570933dd3bcfaa5afbf2 Mon Sep 17 00:00:00 2001 From: priyankagunaki-cloud Date: Wed, 5 Aug 2026 15:03:57 +0530 Subject: [PATCH 3/4] DOC-1153 Update hw-and-sw-requirements.adoc --- .../pages/hw-and-sw-requirements.adoc | 42 +++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/modules/installation/pages/hw-and-sw-requirements.adoc b/modules/installation/pages/hw-and-sw-requirements.adoc index 94969580..eb0a15ff 100644 --- a/modules/installation/pages/hw-and-sw-requirements.adoc +++ b/modules/installation/pages/hw-and-sw-requirements.adoc @@ -220,6 +220,27 @@ Algorithmic workloads, such as PageRank, Community Detection, and Shortest Path, Compression ratio also affects memory and storage requirements. For example, a 10% compression ratio means the stored graph occupies approximately 90% of its original size, while a 40% compression ratio means it occupies approximately 60% of its original size. +==== Memory and Disk Sizing + +Memory recommendations are based on the following workload-specific sizing factors. + +[cols="2,2",options="header"] +|=== +|Workload type +|Memory sizing factor + +|Non-algorithmic +|2 (1x for graph size + 1x for compute) + +|Algorithmic +|3 (1x for graph size + 2x for compute) +|=== + +Disk recommendations: + +* Disk space per node should be approximately equal to the recommended memory per node. +* If the deployment stores multiple backups, large data files, or exported data locally, provision approximately 2× the recommended memory size for disk capacity. + ==== Example The following example demonstrates how workload type and compression ratio affect hardware sizing. @@ -244,27 +265,6 @@ Using the sizing methodology for algorithmic workloads: ** Memory: 900 GB ** Disk per node: ~1.35 TB -==== Memory and Disk Sizing - -Memory recommendations are based on the following workload-specific sizing factors. - -[cols="2,2",options="header"] -|=== -|Workload type -|Memory sizing factor - -|Non-algorithmic -|2 (1x for graph size + 1x for compute) - -|Algorithmic -|3 (1x for graph size + 2x for compute) -|=== - -Disk recommendations: - -* Disk space per node should be approximately equal to the recommended memory per node. -* If the deployment stores multiple backups, large data files, or exported data locally, provision approximately 2× the recommended memory size for disk capacity. - The following estimates represent recommended resources per node for the assumed cluster configuration. .Estimated hardware requirements by dataset size From 5e979544add7efb35cb4b91e672f626a5703f7fe Mon Sep 17 00:00:00 2001 From: priyankagunaki-cloud Date: Wed, 5 Aug 2026 15:30:46 +0530 Subject: [PATCH 4/4] Update hw-and-sw-requirements.adoc --- .../pages/hw-and-sw-requirements.adoc | 20 ++++++++----------- 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/modules/installation/pages/hw-and-sw-requirements.adoc b/modules/installation/pages/hw-and-sw-requirements.adoc index eb0a15ff..47495825 100644 --- a/modules/installation/pages/hw-and-sw-requirements.adoc +++ b/modules/installation/pages/hw-and-sw-requirements.adoc @@ -214,15 +214,15 @@ NOTE: Production systems should consider having replica clusters for redundancy === Hardware Sizing Estimates by Dataset Size -The estimates in this section provide general guidance for sizing hardware based on dataset size, workload type, and compression ratio. They are intended to help with initial capacity planning before selecting hardware for a TigerGraph deployment. +The estimates in this section provide general guidance for sizing hardware based on dataset size, workload type, and compression ratio. They are intended to assist with initial capacity planning for a TigerGraph deployment. Algorithmic workloads, such as PageRank, Community Detection, and Shortest Path, generally require more memory than non-algorithmic workloads because they perform additional graph computations. -Compression ratio also affects memory and storage requirements. For example, a 10% compression ratio means the stored graph occupies approximately 90% of its original size, while a 40% compression ratio means it occupies approximately 60% of its original size. +Compression ratio affects both memory and storage requirements. For example, a 10% compression ratio means the stored graph occupies approximately 90% of its original size, while a 40% compression ratio means it occupies approximately 60% of its original size. ==== Memory and Disk Sizing -Memory recommendations are based on the following workload-specific sizing factors. +The following workload-specific sizing factors are used to estimate memory requirements. [cols="2,2",options="header"] |=== @@ -236,26 +236,23 @@ Memory recommendations are based on the following workload-specific sizing facto |3 (1x for graph size + 2x for compute) |=== -Disk recommendations: +*Disk recommendations* * Disk space per node should be approximately equal to the recommended memory per node. * If the deployment stores multiple backups, large data files, or exported data locally, provision approximately 2× the recommended memory size for disk capacity. ==== Example -The following example demonstrates how workload type and compression ratio affect hardware sizing. +The following example demonstrates how workload type and compression ratio affect hardware sizing. The memory and disk estimates are calculated per node for the assumed 3-node HA cluster. Assumptions: * Raw dataset size: 500 GB -* 3-node HA cluster -* Replication factor: 3 +* HA cluster with 3 nodes * Algorithmic workload * CPU recommendation: Up to 64 cores -The memory and disk estimates in this example are calculated per node. - -Using the sizing methodology for algorithmic workloads: +Using the sizing factors described above: * At a 10% compression ratio: ** Memory: 1.35 TB @@ -265,9 +262,8 @@ Using the sizing methodology for algorithmic workloads: ** Memory: 900 GB ** Disk per node: ~1.35 TB -The following estimates represent recommended resources per node for the assumed cluster configuration. -.Estimated hardware requirements by dataset size +.Estimated hardware requirements per node by dataset size [cols="1,1,1,1,1,1",options="header"] |===