∼ 1 TB/hr ◮ Distinct sources: ∼ 107 − 108, ⊲ 15, 000 measurements per unique source per year ⊲ A measurement is about 300 B ◮ Stored source properties reduce to 50 − 100 TB/yr ◮ Peaks over 10,000 sources per second ◮ n transients per day, n > 0 Bart Scheers | TKP Meeting | 2014-01-09 Transients Database Performance
∼ 1 TB/hr ◮ Distinct sources: ∼ 107 − 108, ⊲ 15, 000 measurements per unique source per year ⊲ A measurement is about 300 B ◮ Stored source properties reduce to 50 − 100 TB/yr ◮ Peaks over 10,000 sources per second ◮ n transients per day, n > 0 ◮ Actively use database ⇒ move algorithms and statistics inside database engine ◮ Real-time data access, quick responses ⇒ single node ◮ Accumulate data over time ⇒ multiple nodes Bart Scheers | TKP Meeting | 2014-01-09 Transients Database Performance
column-store database ◮ Processed 6 series of 1000 images (x axes) ◮ Per series the number of sources varied ◮ Response times of two most intensive queries shown on the y axes. Bart Scheers | TKP Meeting | 2014-01-09 Transients Database Performance
over multiple nodes ◮ Shard by zone/declination ◮ Partition by time ◮ Preferably no code changes Distributed Databases ◮ Use distributed file system ◮ Use intelligence and autonomy of storage devices ◮ Exploit tiers for data summarisations Bart Scheers | TKP Meeting | 2014-01-09 Transients Database Performance
algorithms and operations to the data ◮ Real-time database ⊲ Known TraP queries behave linearly over time ⊲ Adding more statistical functions ◮ Distributed Databases, using intelligence and autonomy of storage devices ⊲ Read-only archive performs acceptible ⊲ Scalable ⊲ Adding more Query Nodes Bart Scheers | TKP Meeting | 2014-01-09 Transients Database Performance