For cassandra, I highly recommend inserting async. Or if you want to keep 
existing code, for the bolt that does the insert make the tasks to executor 
ratio something like 4:1.

From: cogumelosmaravilha 
<[email protected]<mailto:[email protected]>>
Reply-To: "[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Date: Wednesday, May 11, 2016 at 6:02 PM
To: "[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Subject: Re: Benchmarks

No, this is very time consuming!
I need to write the best code, to get the top speed, tunning configuration 
files, chose the best driver, etc.
I'm thinking in Hadhoop and Postgresql. In most of the projects we need an ACID 
and a NoSQL.
Because Storm is so fast i can't send the data in the last Bolt over Internet. 
I'm choosing a "queue" to stop the latency in Storm.

On 11-05-2016 23:08, steve tueno wrote:
Thanks.
Hve you try your benchmark with Hbase?

Cordialement,
TUENO FOTSO STEVE JEFFREY
Élève Ingénieur
5GI ENSP
+237 676 57 17 28
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2016-05-11 22:46 GMT+01:00 cogumelosmaravilha 
<<mailto:[email protected]>[email protected]<mailto:[email protected]>>:
Hi all,
I made some database benchmarks that i want to share. Source code and drivers 
are in Python. Kernel 4.4.10-low-latency. Hardware Core-i7 3.6 32GB Ram.
mock data, record;
571fa68da32119f501015f5f 947a20A0A9c5d28550110E05 e71bB0597363389420459F41 
2016-05-11 11:48:55.948 57 118 01

Network/Wifi:

Records : 100.001

### Mysql Save ###
2016-05-11 16:55:56.342
2016-05-11 17:00:34.401                # 2nd place
time: 278.059647

### Kafka Save ###
2016-05-11 14:25:13.800
2016-05-11 14:26:22.036                # 1st place
time: 68.23572

### Mongo Insert Save ###
2016-05-11 14:26:22.036
2016-05-11 14:36:07.753               # 4
time: 585.71745

### Mongo Upsert Save ###
2016-05-11 14:36:07.754
2016-05-11 14:46:16.527              # 5
time: 608.77346

### Cassandra Save ###
2016-05-11 14:46:16.527
2016-05-11 14:55:18.675              #3rd place
time: 542.148405

Local:

Records : 100001

### Mysql Save ###
2016-05-11 17:07:00.712
2016-05-11 17:07:16.970            # 1st place
time: 16.259082

### Kafka Save ###
2016-05-11 17:12:03.966
2016-05-11 17:12:59.638          # 5
time: 55.672385

### Mongo Insert Save ###
2016-05-11 17:12:59.638
2016-05-11 17:13:33.667          # 2nd place
time: 34.028495

### Mongo Upsert Save ###
2016-05-11 17:13:33.667
2016-05-11 17:14:20.513         # 4
time: 46.846054

### Cassandra Save ###
2016-05-11 17:14:20.513
2016-05-11 17:15:03.807        # 3rd place
time: 43.293934



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