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Benchmarking Couchbase Server
                                                                           Renat Khasanshyn
                                                                    CEO, Altoros Systems, Inc.


CouchConf 2012
September 21, 2012


                      Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Presentation Outline


•   Benchmark Goals
•   Benchmark Design and Scenario
•   Benchmarking Tools
•   Benchmark Results




                                                                                        2
                  Copyright © Altoros Systems, Inc. | CONFIDENTIAL
3
Copyright © Altoros Systems, Inc. | CONFIDENTIAL
About Altoros
 •   Software delivery acceleration specialist for big data application
     implementation services
 •   200+ employees globally (Eastern Europe, US, UK, Denmark, Norway)
 •   Big data practice areas:
       Advertising analytics
       Automated device analytics
       Big data warehouse
Customers




Partners


                                                            Implementation Partner

                                                                                                 4
                         Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Why Benchmark NoSQL technologies?

•   All NoSQL technologies say they are “high
    performance and scalable”
But this isn’t helpful to end users
•   Performance needs to be measured for meaning full
    workloads
     ⇒ To help users understand the performance characteristics of
         databases those workloads

•   So we decided to compare the commonly used
    NoSQL databases
                • MongoDB 2.2RC
                • Cassandra 1.1.2
                • Couchbase Server 2.0 - Recent Build

                                                                          5
                      Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Benchmark Goals


• Reproducible by anyone
          – Open Source workload generator
• Focus on use case for which NoSQL typically
    selected
•   Use a realistic workload
          – Simulate steady state of application running
          – Meaningful data amounts & runtime
• Compare latency vs throughput
• Measure max throughput (for given scenario)


                                                                                      6
                     Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Benchmarking Scenario

•   For interactive web application
       • Scalability and performance are the most common
         requirements
       • Typically leads to users selecting NoSQL over RDBMS
•   The working set of data changes with time
       • End users using the application change over time
       • Example: every few hours, every few days, every few weeks
•   There is more data available than memory (RAM)
•   Replication is used for fault tolerance
•   Real world data sizes
•   Use EC2 as deployment platform
           – Commonly used
           – Easy to replicate results

                                                                                             7
                      Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Benchmarking Scenario Details

Hardware
•   4 Amazon m1.xlarge instances for the NoSQL DBs
•   1 instance used as the client
Workload details
•   Operations are a mix of C:R:U:D in the ratio 5:60:33:2
•   Each document roughly 1.5-2K in size (15 fields * 100 bytes)
•   15 million active and 15 million replica documents
•   Workload with sliding working set
•   Load phase, warm-up phase, access phase
•   Runtime of the access phase ~1 hour
•   Latency measured for varying throughput - 3 times for each run
•   Focus on transaction performance
            – Latency
            – Throughput

                                                                                      8
                      Copyright © Altoros Systems, Inc. | CONFIDENTIAL
What was measured?


• Latency                                           • Throughput
    • Round trip time taken                                 • Throughput was varied
        for a request to execute                                  from 1K ops/sec to 25K
        from the client to the                                    ops/sec depending on
        server and back                                           NoSQL database
    •   Average, 95th and 99th                              •     Max throughput was
        percentile measured                                       measured
•   Why is this important?                          •     Why is this important?
    • You want your users to                                • You want your app to
        have a great experience                                   support hundreds of
    •   Not just an “average”                                     thousands of users
        one
                 Workloads are not rate limited, focused on
                 max throughput.

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                      Copyright © Altoros Systems, Inc. | CONFIDENTIAL
YCSB


                                                          10
       Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Benchmark Implementation: YCSB


•   Yahoo! team offered a “standard” benchmark

•   Yahoo! Cloud Serving Benchmark (YCSB)
          – Focus on database
          – Focus on performance


•   YCSB Client consists of 2 parts
          – Workload generator
          – Workload scenarios




                                                                            11
                  Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Why YCSB


• Open source
• Extensible
• Rich selection of connectors
       •   Azure, BigTable, Cassandra, CouchDB,
       •   Dynomite, GemFire, HBase, Hypertable,
       •   Infinispan, MongoDB, PNUTS, Redis,
       •   Connector for Sharded RDBMS (i.e. MySQL),
       •   Voldemort, GigaSpaces XAP
• We developed a few connectors
       •   Accumulo, Couchbase, Riak,
       •   Connector for Shared Nothing RDBMS (i.e. MySQL Cluster)



                                                                             12
                   Copyright © Altoros Systems, Inc. | CONFIDENTIAL
How YCSB Works




                                                                13
Copyright © Altoros Systems, Inc. | CONFIDENTIAL
THE CONFIGURATIONS


                                                          14
       Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Cluster specification


               Amazon m1.xlarge Instance

                  15 GB memory
                  4 virtual cores
                  4 EBS 50 GB volumes in RAID0
 YCSB Client      64-bit Amazon Linux (CentOS binary compatible)



                                                Amazon m1.xlarge Instances * 4

                                                      15 GB memory
                                                      4 virtual cores
                                                      4 EBS 50 GB volumes in RAID0
                                                      64-bit Amazon Linux


* Extra nodes for masters, routers, etc
                                                                                               15
                    Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Couchbase Configuration


• 4 node Couchbase cluster
• 1 replica setting
• Each node has some active and some replica
    data
•   12GB used as the (12288 MB) Couchbase
    bucket size per node




                                                                                       16
              Copyright © Altoros Systems, Inc. | CONFIDENTIAL
MongoDB Configation


• 4 shards each has 1 replica (replication factor – 1),
    where each shard is a set of 2 nodes - primary and
    secondary
•   Journaling disabled (trying to maximize performance)
•   var shards = [
         "shard1/ycsb-node1:27017,ycsb-node2:27018",
         "shard2/ycsb-node2:27017,ycsb-node1:27018",
         "shard3/ycsb-node3:27017,ycsb-node4:27018",
         "shard4/ycsb-node4:27017,ycsb-node3:27018"];
    Each node running
     • 2 mongod processes (all together 8 mongod
       processes on 4 nodes)
     • 4 mongos processes, which is the MongoDB router,
       process on 27019 port
                                                                                      17
                 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Cassandra Configuration




• Cassandra JVM settings:
  • 1.1) MAX_HEAP_SIZE, which is a total amount of
    memory dedicated to the Java heap - 6G
  • 1.2) HEAP_NEWSIZE, total amount of memory for the
    new generation of objects - 400M


• Cassandra settings:
  • 2.1) RandomPartitioner was used which distributes
    rows across the cluster evenly by MD5
  • 2.2) Memtable size 4048 MB


                                                                                       18
              Copyright © Altoros Systems, Inc. | CONFIDENTIAL
THE RESULTS


                                                          19
       Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Reads (Average time)

                                                    Read latencies against throughput
                       7



                       6

                                                                                        Cassandra
                       5
Average Latency [ms]




                       4
                                      MongoDB
                       3



                       2



                       1
                                                                                                                Couchbase
                       0
                           0   2000   4000   6000        8000        10000        12000        14000    16000    18000   20000   22000
                                                                  Operations per Second



                                                                                                                                         20
                                                     Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Reads (95th percentile)
                               18
                                                      Read latencies against throughput
                               16



                               14
                                                                                            Cassandra
                               12
95th Percentile Latency [ms]




                               10



                                8



                                6



                                4                                                                                       Couchbase
                                2
                                                             MongoDB
                                0
                                    0   2000   4000   6000        8000        10000        12000           14000     16000   18000   20000   22000
                                                                         Operations per Second


                                                                                                                                               21
                                                        Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Reads (99th percentile)
                               60
                                                        Read latencies against throughput

                               50



                                                                                            Cassandra
                               40
99th Percentile Latency [ms]




                               30              MongoDB

                               20




                               10
                                                                                                                    Couchbase

                                0
                                    0   2000     4000   6000        8000        10000        12000          14000     16000   18000   20000   22000
                                                                            Operations per Second


                                                                                                                                                22
                                                         Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Mongo Replica Reads


•   MongoDB setup had 4 shards
    • By default only masters will service reads
•   To allow replica reads and still be comparable, need to
    ensure that replica data is up-to-date
     • This was done using write-concern (REPLICAS_SAFE)
•   Tests showed that results did not improve
     • This includes results for writes




                                                                                         23
                    Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Writes (Average time)
                        5
                                           Insert and Update latencies against throughput
                       4.5


                        4

                                 MongoDB
                       3.5
Average Latency [ms]




                        3                                                         Cassandra
                       2.5


                        2


                       1.5


                        1
                                                                                         Couchbase
                       0.5


                        0
                             0   2000   4000    6000        8000         10000        12000           14000    16000   18000   20000   22000
                                                                   Operations per second


                                                                                                                                         24
                                                   Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Writes (95th percentile)
                               30
                                                       Insert and update latencies against throughput

                               25



                                               MongoDB
95th Percentile Latency [ms]




                               20




                               15

                                                                         Cassandra
                               10




                                                                                                            Couchbase
                                5




                                0
                                    0   2000    4000       6000           8000         10000        12000            14000      16000   18000   20000   22000
                                                                                 Operations per Second




                                                                                                                                                          25
                                                                  Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Writes (99th percentile)
                               50
                                                      Insert and update latencies against throughput
                               45


                               40




                                           MongoDB
                               35
99th Percentile Latency [ms]




                               30


                               25
                                                                         Cassandra
                               20


                               15


                               10


                                5
                                                                                                            Couchbase
                                0
                                    0   2000   4000     6000         8000        10000        12000       14000   16000   18000   20000   22000
                                                                            Operations per Second


                                                                                                                                                  26
                                                               Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Results Analysis

•   Couchbase
        • Showed the lowest latencies & highest throughput
        • Latency was independent of throughput for up to 3/4th the max
          achievable throughput (for both reads and write)
•   Cassandra
        • Had the highest latencies of all the databases
        • Showed higher max throughput compared with mongoDB but only
          60% of the throughput achieved by Couchbase
        • Latencies rose fast as throughput was increased
•   MongoDB
        • Read latencies were better than Cassandra but higher than
          Couchbase
        • Max throughput for read and writes was the lowest of all the
          databases
            – Particularly for writes, high latencies seen for average throughput
            – Coarse write lock seems to have a big impact on performance


                                                                                           27
                         Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Other Thoughts

•   You decide who is a winner
•   NoSQL is a “different horses for different courses”
•   Evaluate before choosing the “horse”
•   Construct your own or use existing workloads
     • Benchmark it
     • Tune database!
     • Benchmark it again

Amazon EC2 observations
• Scales perfectly for NoSQL
• EBS slows down database on reads
• RAID0 it! Use 4 disk in array (good choice), some reported
  performance degraded with higher number (6 and >)




                                                                                     28
                      Copyright © Altoros Systems, Inc. | CONFIDENTIAL
What are we missing in our benchmarking scenario?

Load phase workload
• Working set is created
• 15 million records
• 1.5 KB record (15 fields by 100 Bytes)
• 45GB total or ≈12GB per node
Ideas, anyone?




                                                                        29
                     Copyright © Altoros Systems, Inc. | CONFIDENTIAL
YCSB Connectors




github.com/Altoros/YCSB




                                                                     30
    Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Workload Generator Specs


Hotspot generator with sliding window:

hotspotslidingspeed=10
Speed of the hot set window movement measured in keys per second, with a
default value of 10 keys/sec (can be overridden in workload properties file).
hotspotdatafraction=0.2
Proportion of the hot data set to the whole dataset, default is 0.2
hotspotoperationfraction=0.9
Value specifying how often hot dataset will be queried comparing to cold
dataset, default is 0.8, used 0.9
lowerbound=0
The minimal key value allowed to be queried. Set to 0
upperbound=15000000
The maximum key value allowed to be queried. Set to 15 million

Also specification of the client process, which drives workload:
6) threadcount=30
Number of parallel threads spawned on the client node to drive benchmark

                                                                                             31
                        Copyright © Altoros Systems, Inc. | CONFIDENTIAL
Thank you!




Thank You!
   renat.k@altoros.com
    @renatkhasanshyn
    Tel. (650) 395-7002




                                                            32
Copyright © Altoros Systems, Inc. | CONFIDENTIAL

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Couchbase Performance Benchmarking

  • 1. Benchmarking Couchbase Server Renat Khasanshyn CEO, Altoros Systems, Inc. CouchConf 2012 September 21, 2012 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 2. Presentation Outline • Benchmark Goals • Benchmark Design and Scenario • Benchmarking Tools • Benchmark Results 2 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 3. 3 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 4. About Altoros • Software delivery acceleration specialist for big data application implementation services • 200+ employees globally (Eastern Europe, US, UK, Denmark, Norway) • Big data practice areas:  Advertising analytics  Automated device analytics  Big data warehouse Customers Partners Implementation Partner 4 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 5. Why Benchmark NoSQL technologies? • All NoSQL technologies say they are “high performance and scalable” But this isn’t helpful to end users • Performance needs to be measured for meaning full workloads ⇒ To help users understand the performance characteristics of databases those workloads • So we decided to compare the commonly used NoSQL databases • MongoDB 2.2RC • Cassandra 1.1.2 • Couchbase Server 2.0 - Recent Build 5 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 6. Benchmark Goals • Reproducible by anyone – Open Source workload generator • Focus on use case for which NoSQL typically selected • Use a realistic workload – Simulate steady state of application running – Meaningful data amounts & runtime • Compare latency vs throughput • Measure max throughput (for given scenario) 6 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 7. Benchmarking Scenario • For interactive web application • Scalability and performance are the most common requirements • Typically leads to users selecting NoSQL over RDBMS • The working set of data changes with time • End users using the application change over time • Example: every few hours, every few days, every few weeks • There is more data available than memory (RAM) • Replication is used for fault tolerance • Real world data sizes • Use EC2 as deployment platform – Commonly used – Easy to replicate results 7 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 8. Benchmarking Scenario Details Hardware • 4 Amazon m1.xlarge instances for the NoSQL DBs • 1 instance used as the client Workload details • Operations are a mix of C:R:U:D in the ratio 5:60:33:2 • Each document roughly 1.5-2K in size (15 fields * 100 bytes) • 15 million active and 15 million replica documents • Workload with sliding working set • Load phase, warm-up phase, access phase • Runtime of the access phase ~1 hour • Latency measured for varying throughput - 3 times for each run • Focus on transaction performance – Latency – Throughput 8 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 9. What was measured? • Latency • Throughput • Round trip time taken • Throughput was varied for a request to execute from 1K ops/sec to 25K from the client to the ops/sec depending on server and back NoSQL database • Average, 95th and 99th • Max throughput was percentile measured measured • Why is this important? • Why is this important? • You want your users to • You want your app to have a great experience support hundreds of • Not just an “average” thousands of users one Workloads are not rate limited, focused on max throughput. 9 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 10. YCSB 10 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 11. Benchmark Implementation: YCSB • Yahoo! team offered a “standard” benchmark • Yahoo! Cloud Serving Benchmark (YCSB) – Focus on database – Focus on performance • YCSB Client consists of 2 parts – Workload generator – Workload scenarios 11 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 12. Why YCSB • Open source • Extensible • Rich selection of connectors • Azure, BigTable, Cassandra, CouchDB, • Dynomite, GemFire, HBase, Hypertable, • Infinispan, MongoDB, PNUTS, Redis, • Connector for Sharded RDBMS (i.e. MySQL), • Voldemort, GigaSpaces XAP • We developed a few connectors • Accumulo, Couchbase, Riak, • Connector for Shared Nothing RDBMS (i.e. MySQL Cluster) 12 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 13. How YCSB Works 13 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 14. THE CONFIGURATIONS 14 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 15. Cluster specification Amazon m1.xlarge Instance 15 GB memory 4 virtual cores 4 EBS 50 GB volumes in RAID0 YCSB Client 64-bit Amazon Linux (CentOS binary compatible) Amazon m1.xlarge Instances * 4 15 GB memory 4 virtual cores 4 EBS 50 GB volumes in RAID0 64-bit Amazon Linux * Extra nodes for masters, routers, etc 15 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 16. Couchbase Configuration • 4 node Couchbase cluster • 1 replica setting • Each node has some active and some replica data • 12GB used as the (12288 MB) Couchbase bucket size per node 16 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 17. MongoDB Configation • 4 shards each has 1 replica (replication factor – 1), where each shard is a set of 2 nodes - primary and secondary • Journaling disabled (trying to maximize performance) • var shards = [ "shard1/ycsb-node1:27017,ycsb-node2:27018", "shard2/ycsb-node2:27017,ycsb-node1:27018", "shard3/ycsb-node3:27017,ycsb-node4:27018", "shard4/ycsb-node4:27017,ycsb-node3:27018"]; Each node running • 2 mongod processes (all together 8 mongod processes on 4 nodes) • 4 mongos processes, which is the MongoDB router, process on 27019 port 17 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 18. Cassandra Configuration • Cassandra JVM settings: • 1.1) MAX_HEAP_SIZE, which is a total amount of memory dedicated to the Java heap - 6G • 1.2) HEAP_NEWSIZE, total amount of memory for the new generation of objects - 400M • Cassandra settings: • 2.1) RandomPartitioner was used which distributes rows across the cluster evenly by MD5 • 2.2) Memtable size 4048 MB 18 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 19. THE RESULTS 19 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 20. Reads (Average time) Read latencies against throughput 7 6 Cassandra 5 Average Latency [ms] 4 MongoDB 3 2 1 Couchbase 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 Operations per Second 20 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 21. Reads (95th percentile) 18 Read latencies against throughput 16 14 Cassandra 12 95th Percentile Latency [ms] 10 8 6 4 Couchbase 2 MongoDB 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 Operations per Second 21 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 22. Reads (99th percentile) 60 Read latencies against throughput 50 Cassandra 40 99th Percentile Latency [ms] 30 MongoDB 20 10 Couchbase 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 Operations per Second 22 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 23. Mongo Replica Reads • MongoDB setup had 4 shards • By default only masters will service reads • To allow replica reads and still be comparable, need to ensure that replica data is up-to-date • This was done using write-concern (REPLICAS_SAFE) • Tests showed that results did not improve • This includes results for writes 23 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 24. Writes (Average time) 5 Insert and Update latencies against throughput 4.5 4 MongoDB 3.5 Average Latency [ms] 3 Cassandra 2.5 2 1.5 1 Couchbase 0.5 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 Operations per second 24 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 25. Writes (95th percentile) 30 Insert and update latencies against throughput 25 MongoDB 95th Percentile Latency [ms] 20 15 Cassandra 10 Couchbase 5 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 Operations per Second 25 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 26. Writes (99th percentile) 50 Insert and update latencies against throughput 45 40 MongoDB 35 99th Percentile Latency [ms] 30 25 Cassandra 20 15 10 5 Couchbase 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 Operations per Second 26 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 27. Results Analysis • Couchbase • Showed the lowest latencies & highest throughput • Latency was independent of throughput for up to 3/4th the max achievable throughput (for both reads and write) • Cassandra • Had the highest latencies of all the databases • Showed higher max throughput compared with mongoDB but only 60% of the throughput achieved by Couchbase • Latencies rose fast as throughput was increased • MongoDB • Read latencies were better than Cassandra but higher than Couchbase • Max throughput for read and writes was the lowest of all the databases – Particularly for writes, high latencies seen for average throughput – Coarse write lock seems to have a big impact on performance 27 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 28. Other Thoughts • You decide who is a winner • NoSQL is a “different horses for different courses” • Evaluate before choosing the “horse” • Construct your own or use existing workloads • Benchmark it • Tune database! • Benchmark it again Amazon EC2 observations • Scales perfectly for NoSQL • EBS slows down database on reads • RAID0 it! Use 4 disk in array (good choice), some reported performance degraded with higher number (6 and >) 28 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 29. What are we missing in our benchmarking scenario? Load phase workload • Working set is created • 15 million records • 1.5 KB record (15 fields by 100 Bytes) • 45GB total or ≈12GB per node Ideas, anyone? 29 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 30. YCSB Connectors github.com/Altoros/YCSB 30 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 31. Workload Generator Specs Hotspot generator with sliding window: hotspotslidingspeed=10 Speed of the hot set window movement measured in keys per second, with a default value of 10 keys/sec (can be overridden in workload properties file). hotspotdatafraction=0.2 Proportion of the hot data set to the whole dataset, default is 0.2 hotspotoperationfraction=0.9 Value specifying how often hot dataset will be queried comparing to cold dataset, default is 0.8, used 0.9 lowerbound=0 The minimal key value allowed to be queried. Set to 0 upperbound=15000000 The maximum key value allowed to be queried. Set to 15 million Also specification of the client process, which drives workload: 6) threadcount=30 Number of parallel threads spawned on the client node to drive benchmark 31 Copyright © Altoros Systems, Inc. | CONFIDENTIAL
  • 32. Thank you! Thank You! renat.k@altoros.com @renatkhasanshyn Tel. (650) 395-7002 32 Copyright © Altoros Systems, Inc. | CONFIDENTIAL