PHPMem v2.0.1

Version
1.6.45
Uptime
7 days 2 hours 56 minutes 23 seconds

Memory

Total
512MB
Used
13,32MB (2.6%)
Free
498,68MB

Keys

Current
10 162
Total (since start)
11 092
Evictions
0
Reclaimed
157
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
4 / 1 024 max
Total
51 279
Rejected
0
llm:f1818b58ce0352b57ab50326e47fc9760d36f54a191b01dce4bb54fa8701c240
TTL 14 hours 56 minutes 9 seconds Size 2,15KB Export
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### 1.1 Business Data Landscape This dataset represents passenger manifest and outcome data from the RMS Titanic maritime disaster, structured as a single comprehensive dimension table. The **titanic** table captures 891 individual passenger records, combining demographic attributes, ticketing information, cabin assignments, and survival outcomes in one unified entity. This is a historical research dataset rather than an operational business system, designed to support retrospective analysis of passenger characteristics and survival patterns. The single-table architecture reflects a snapshot analytical use case where all relevant passenger attributes have been denormalized into one queryable asset. ### 1.2 Key Data Assets | Table | Role | Rows | % of Total | Size | |-------|------|-----:|----------:|-----:| | titanic | dimension | 891 | 100.0% | 0.2 MB | The **titanic** table constitutes the entire data estate, holding all 891 passenger records in a 0.2 MB footprint. This table serves as both the analytical foundation and the complete inventory of passengers, eliminating the need to navigate multiple entities or resolve joins to answer questions about passenger demographics, ticket classes, family composition, or survival rates. ### 1.3 Data Integration Posture With zero inter-table relationships detected, this dataset operates as a self-contained analytical unit rather than an integrated data ecosystem. The absence of foreign keys or related tables means all analysis occurs within the boundaries of the single passenger manifest—there are no external reference tables for ports of embarkation, crew rosters, or voyage details that would require cross-functional joins. This posture simplifies immediate query patterns but limits the ability to enrich passenger records with contextual data from complementary sources; any expansion of analytical scope would require ingesting and linking additional datasets such as historical fare schedules, ship deck layouts, or lifeboat capacity records to move beyond the current passenger-centric view.