13. 1 TRILLION ROWS PER SECOND
• 12 Intel servers running MemSQL
• Scale-out architecture
• Operations on encoded data
• Query Vectorization
with GROUP BY using SIMD and AVX-2
14. 1 TRILLION ROWS PER SECOND
DATA
• 115 billion rows simulated NASDAQ trades
• Columnstore on-disk workload
• Partitioned by stock_symbol, key(stock_symbol)
HARDWARE
• 12 Intel servers, each with:
• 2 Skylake Processors, 26 cores per chip
• Numa enabled and 616 leaf cores
15. 1 TRILLION ROWS PER SECOND
QUERY “TOP 10 most traded stocks”
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 [Grey’s] 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