DBMS vs. DSMS DBMS DSMS Data persistent relations streams Data Access random sequential, single-pass Updates arbitrary append-only Update rates relatively low high, bursty Processing Model query-driven Kalavri | Boston University 2020 Stream synopses requirements • Single-pass: synopses can be easily updated with a single pass over streaming tuples in their arrival order • Small space: memory footprint

for every new edge? • Can we use graph synopses and summaries and compute graph analytics in one-pass? ??? Vasiliki Kalavri | Boston University 2020 Connectivity & Bipartite property 23 ??? Vasiliki structure for the components • it stores a set of elements partitioned in disjoint subsets • Single-pass computation: For each edge • if seen for the 1st time, create a component with ID the min of the

of the values • the number of observations We can compute the three summary values in a single pass through the data. • μ = sum / count • var = (sum of squares / count) - μ2 Then var = ∑ (xi − of the values • the number of observations We can compute the three summary values in a single pass through the data. • μ = sum / count • var = (sum of squares / count) - μ2 Then var = ∑ (xi −

7/site-packages/ ˓→pyflink/table/utils.py:55: FutureWarning: Schema passed to names= option, please pass␣ ˓→schema= explicitly. Will raise exception in future return pa.RecordBatch.from_arrays(arrays, schema) 7/site-packages/ ˓→pyflink/table/utils.py:55: FutureWarning: Schema passed to names= option, please pass␣ ˓→schema= explicitly. Will raise exception in future return pa.RecordBatch.from_arrays(arrays, schema)

7/site-packages/ ˓→pyflink/table/utils.py:55: FutureWarning: Schema passed to names= option, please pass␣ ˓→schema= explicitly. Will raise exception in future return pa.RecordBatch.from_arrays(arrays, schema) 7/site-packages/ ˓→pyflink/table/utils.py:55: FutureWarning: Schema passed to names= option, please pass␣ ˓→schema= explicitly. Will raise exception in future return pa.RecordBatch.from_arrays(arrays, schema)