13. 1 TRILLION ROWS PER SECOND
âą 12 Intel servers running MemSQL
âą Scale-out architecture
âą Operations on encoded data
âą Query Vectorization
with GROUP BY using SIMD and AVX-2
14. 1 TRILLION ROWS PER SECOND
DATA
âą 115 billion rows simulated NASDAQ trades
âą Columnstore on-disk workload
âą Partitioned by stock_symbol, key(stock_symbol)
HARDWARE
âą 12 Intel servers, each with:
âą 2 Skylake Processors, 26 cores per chip
âą Numa enabled and 616 leaf cores
15. 1 TRILLION ROWS PER SECOND
QUERY âTOP 10 most traded stocksâ
SELECT stock_symbol, COUNT (*)
FROM trade
GROUP BY stock_symbol
ORDER BY c desc LIMIT 10;
DATA SIZE
SELECT FORMAT (COUNT (*), 0)
as row_count
FROM trade;
row_count 115,587,416,064
+--------------+----------+
| stock_symbol | c |
+--------------+----------+
| AAPL | 78905344 |
| AMGN | 78905344 |
| BIDU | 78643200 |
| CSCO | 78643200 |
| KHC | 78381056 |
| CHTR | 78381056 |
| QCOM | 78381056 |
| CELG | 78381056 |
| FB | 78381056 |
| GOOG | 78381056 |
+--------------+----------+
10 rows in set (0.10 sec)
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23. Picking an operational, latency-free platform
For the front lines of the business
Keep pace with operational data (inserts, updates, deletes)
Provide easy compatibility within the data ecosystem
Focused on value and infrasturcutre consolidation
24. Analytics in Real Time,
the [Greyâs] Anatomy
of Event Streaming
with guest from Disney ABC TV
TODAY | ROOM 230B | 11:50 AM
25. On the journey to latency-free
our choices are here today