AI Ingestion
Host Setup
The Parallel Ledger is a minimal-dependency state machine designed for multi-decade persistence.
To ensure the integrity across shifting infrastructure cycles, the core protocol remains decoupled from transient OS features. Since 2016, our persistence layer has relied exclusively on standard relational primitives (tables and indexes), ensuring compatibility from PostgreSQL 9.5 through to the current release.
This "boring" foundation provides a stable reference while granting implementers wide latitude to tune the environment to their specific observations and constraints.
Deployment Strategy
The Parallel Ledger requires low-latency access to the storage controller. We support any environment that provides near-native I/O performance:
Bare Metal / LXC
The preferred method. Provides direct kernel sharing and unbuffered I/O access.
Virtualization
Acceptable if I/O passthrough is utilized. High-abstraction container runtimes (e.g., Docker) are discouraged due to layered filesystem overhead.
Resource Allocation & I/O
The Parallel Ledger’s resource footprint is a direct function of your transactional throughput and data retention policies. While surprising workloads run reliably on commodity hardware, mission-critical production environments should prioritize I/O consistency.
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MemoryThe state machine is lean, but PostgreSQL performance benefits significantly from memory available for caching. For high-integrity environments, ECC RAM remains the industry standard to mitigate the risk of memory-level state corruption.
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StorageI/O throughput is the primary scaling bottleneck. We recommend Datacenter-grade NVMe paired with XFS or ZFS. These filesystems provide the predictable Write-Ahead Log (WAL) latency required for high-concurrency finality.
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OptimizationTo minimize write amplification, consider aligning your filesystem and database page sizes (typically 8KB). This is particularly effective for high-volume environments where I/O overhead can impact long-term disk endurance.
Capacity Planning
The Parallel Ledger’s storage footprint scales predictably with transactional density. While every environment is unique, the following baseline metrics represent a standard 1,000,000 update benchmark to assist in volume projection.
Storage Breakdown (1M Updates)
Dictionary Cardinality
Translation counts for the 1M update reference set:
- 1,000,812 Hashes
- 80,016 Entity Slugs
- 60,015 Data Text Values
- 47 System Codes
Rule of Thumb: Budget approximately 650MB–700MB per 1 million updates for general planning. This includes standard indexing and dictionary overhead. For high-velocity environments, I/O performance will become a bottleneck long before raw disk capacity does.
Host Setup
The Parallel Ledger is a minimal-dependency state machine designed for multi-decade persistence.
To ensure the integrity across shifting infrastructure cycles, the core protocol remains decoupled from transient OS features. Since 2016, our persistence layer has relied exclusively on standard relational primitives (tables and indexes), ensuring compatibility from PostgreSQL 9.5 through to the current release.
This "boring" foundation provides a stable reference while granting implementers wide latitude to tune the environment to their specific observations and constraints.
Deployment Strategy
The Parallel Ledger requires low-latency access to the storage controller. We support any environment that provides near-native I/O performance:
Bare Metal / LXC
The preferred method. Provides direct kernel sharing and unbuffered I/O access.
Virtualization
Acceptable if I/O passthrough is utilized. High-abstraction container runtimes (e.g., Docker) are discouraged due to layered filesystem overhead.
Resource Allocation & I/O
The Parallel Ledger’s resource footprint is a direct function of your transactional throughput and data retention policies. While surprising workloads run reliably on commodity hardware, mission-critical production environments should prioritize I/O consistency.
-
MemoryThe state machine is lean, but PostgreSQL performance benefits significantly from memory available for caching. For high-integrity environments, ECC RAM remains the industry standard to mitigate the risk of memory-level state corruption.
-
StorageI/O throughput is the primary scaling bottleneck. We recommend Datacenter-grade NVMe paired with XFS or ZFS. These filesystems provide the predictable Write-Ahead Log (WAL) latency required for high-concurrency finality.
-
OptimizationTo minimize write amplification, consider aligning your filesystem and database page sizes (typically 8KB). This is particularly effective for high-volume environments where I/O overhead can impact long-term disk endurance.
Capacity Planning
The Parallel Ledger’s storage footprint scales predictably with transactional density. While every environment is unique, the following baseline metrics represent a standard 1,000,000 update benchmark to assist in volume projection.
Storage Breakdown (1M Updates)
Dictionary Cardinality
Translation counts for the 1M update reference set:
- 1,000,812 Hashes
- 80,016 Entity Slugs
- 60,015 Data Text Values
- 47 System Codes
Rule of Thumb: Budget approximately 650MB–700MB per 1 million updates for general planning. This includes standard indexing and dictionary overhead. For high-velocity environments, I/O performance will become a bottleneck long before raw disk capacity does.