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Cassandra performance tuning from Jon Haddad, Principal Consultant at The Last Pickle.
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Cassandra Core Concepts - Cassandra Day Toronto
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Jon Haddad
Diagnosing problems in production. JVM tuning, Cassandra monitoring, disk, cpu, linux tools.
Diagnosing Problems in Production (Nov 2015)
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An introduction to core concepts in Apache Cassandra. We cover the evolution of database architecture as you try to scale a relational database to solve big data problems, and explain how Cassandra handles these problems efficiently.
Cassandra Core Concepts
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Discussion of PySpark, Dataframes, RDD and Cassandra. Batch processing, stream processing, machine learning, collaborative filtering, alternating least squares, recommendation engine, pandas, matplotlib, numpy, seaborn, ipython notebooks, sql
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Jon Haddad
From the original abstract: If you're already using Cassandra you're already aware of it’s strengths of high availability and linear scalability. The downside to this power is less query flexibility. For an OLTP system with an SLA this is an acceptable tradeoff, but for a data scientist it’s extremely limiting. Enter Apache Spark. Apache spark complements an existing Cassandra cluster by providing a means of executing arbitrary queries, filters, sorting and aggregation. It’s possible to use functional constructs like map, filter, and reduce, as well as SQL and DataFrames. In this presentation I’ll show you how to process Cassandra data in bulk or through a Kafka stream using Python. Then we’ll visualize our data using iPython notebooks, leveraging Pandas and matplotlib. This is an advanced talk. We will assume existing knowledge of Cassandra and CQL.
Intro to py spark (and cassandra)
Intro to py spark (and cassandra)
Jon Haddad
These are the slides from my talk at Hulu in March 2015 discussing Apache Spark & Cassandra. I cover the evolution of data from a single machine to RDBMS (MySQL is the primary example) to big data systems. On the Spark side, I covered batch jobs, streaming, Apache Kafka, an introduction to machine learning, clustering, logistic regression and recommendations systems (collaborative filtering). The talk was recorded and is available on youtube: https://www.youtube.com/watch?v=_gFgU3phogQ
Spark and cassandra (Hulu Talk)
Spark and cassandra (Hulu Talk)
Jon Haddad
Intro deck from Cassandra Day Atlanta. Covers the evolution of data storage and analysis, the architecture of Cassandra, the read & write path, and using Cassandra for analytics. By Jon Haddad & Luke Tillman
Intro to Cassandra
Intro to Cassandra
Jon Haddad
python & cassandra intro
Python and cassandra
Python and cassandra
Jon Haddad
These slides are part of a presentation I gave on a Google Hangout on air regarding Python Performance Profiling. Specifically, I explore examining both development and production environments, build systems, testing frameworks (py.test & nose), various profilers for dev, and how to profile in production. The full talk is on youtube here: https://www.youtube.com/watch?v=tZc-v0-3OKQ
Python performance profiling
Python performance profiling
Jon Haddad
This presentation covers diagnosing and solving common problems encountered in production, using performance profiling tools. We’ll also give a crash course to basic JVM garbage collection tuning. Readers will leave with a better understanding of what they should look for when they encounter problems with their in-production Cassandra cluster. This presentation is intended for people with a general understanding of Cassandra, but it not required to have experience running it in production.
Diagnosing Problems in Production - Cassandra
Diagnosing Problems in Production - Cassandra
Jon Haddad
Python is a great programming language that works great with Cassandra. If your goal is to get your project into production quickly and iterate fast, Python is a great solution. These slides are an introduction to the hands on portion from GitHub. https://github.com/rustyrazorblade/python-presentation
Python & Cassandra - Best Friends
Python & Cassandra - Best Friends
Jon Haddad
Here's the slides from the intro talk Luke Tillman and I gave at Cassandra Day Denver.
Introduction to Cassandra - Denver
Introduction to Cassandra - Denver
Jon Haddad
At the 2014 Cassandra summit we covered how to ensure that your production experience with Cassandra is top notch by identifying the proper tools that should be put in place beforehand, and what tools you need to identify problems in real time. Presented by Jon Haddad & Blake Eggleston
Diagnosing Problems in Production: Cassandra Summit 2014
Diagnosing Problems in Production: Cassandra Summit 2014
Jon Haddad
Crash course intro to cassandra
Crash course intro to cassandra
Jon Haddad
Slides from our presentation at the Santa Monica Coloft on our Migration from MongoDB to Cassandra.
Cassandra meetup slides - Oct 15 Santa Monica Coloft
Cassandra meetup slides - Oct 15 Santa Monica Coloft
Jon Haddad
More from Jon Haddad
(17)
Cassandra Performance Tuning Like You've Been Doing It for Ten Years
Cassandra Performance Tuning Like You've Been Doing It for Ten Years
Cassandra Core Concepts - Cassandra Day Toronto
Cassandra Core Concepts - Cassandra Day Toronto
Diagnosing Problems in Production (Nov 2015)
Diagnosing Problems in Production (Nov 2015)
Cassandra Core Concepts
Cassandra Core Concepts
Enter the Snake Pit for Fast and Easy Spark
Enter the Snake Pit for Fast and Easy Spark
Cassandra 3.0 Awesomeness
Cassandra 3.0 Awesomeness
Intro to py spark (and cassandra)
Intro to py spark (and cassandra)
Spark and cassandra (Hulu Talk)
Spark and cassandra (Hulu Talk)
Intro to Cassandra
Intro to Cassandra
Python and cassandra
Python and cassandra
Python performance profiling
Python performance profiling
Diagnosing Problems in Production - Cassandra
Diagnosing Problems in Production - Cassandra
Python & Cassandra - Best Friends
Python & Cassandra - Best Friends
Introduction to Cassandra - Denver
Introduction to Cassandra - Denver
Diagnosing Problems in Production: Cassandra Summit 2014
Diagnosing Problems in Production: Cassandra Summit 2014
Crash course intro to cassandra
Crash course intro to cassandra
Cassandra meetup slides - Oct 15 Santa Monica Coloft
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Performance tuning
1.
JON HADDAD PRINCIPAL CONSULTANT,
THE LAST PICKLE CASSANDRA PERFORMANCE TUNING
2.
WHY AM I HERE?
3.
ACT 1: MEASURE
4.
THROUGHPUT, LATENCY, ERROR RATE
5.
REQUESTS / SECOND
6.
1MS P99
7.
WE NEED A METHODOLOGY
8.
OPERATING SYSTEM
9.
WHAT’S THE BOTTLENECK?
10.
DISK IO NETWORK IO CPU
USAGE
11.
12.
CASSANDRA HAS METRICS
13.
JMX
14.
NODETOOL
15.
15 $ nodetool tablehistograms
movielens ratings_by_user movielens/ratings_by_user histograms Percentile SSTables Write Latency Read Latency Partition Size Cell Count (micros) (micros) (bytes) 50% 0.00 17.08 0.00 3973 215 75% 0.00 20.50 0.00 9887 446 95% 0.00 29.52 0.00 20501 1109 98% 0.00 42.51 0.00 24601 1331 99% 0.00 51.01 0.00 29521 1597 Min 0.00 5.72 0.00 925 51 Max 0.00 43388.63 0.00 51012 2299
16.
15 $ nodetool tablehistograms
movielens ratings_by_user movielens/ratings_by_user histograms Percentile SSTables Write Latency Read Latency Partition Size Cell Count (micros) (micros) (bytes) 50% 0.00 17.08 0.00 3973 215 75% 0.00 20.50 0.00 9887 446 95% 0.00 29.52 0.00 20501 1109 98% 0.00 42.51 0.00 24601 1331 99% 0.00 51.01 0.00 29521 1597 Min 0.00 5.72 0.00 925 51 Max 0.00 43388.63 0.00 51012 2299
17.
18.
VISUALIZE METRICS
19.
20.
ANALYZE CALL TREES AND
INTERNALS
21.
JAVA FLIGHT RECORDER
22.
YOURKIT
23.
24.
FLAME GRAPHS
25.
26.
ACT 2: COMMON OPTIMIZATIONS
27.
READ HEAVY? FIX YOUR
READ AMPLIFICATION
28.
COMPRESSION
29.
DEFAULTS ARE MEH
30.
CHUNK SIZE (64K)
31.
30 $ nodetool cfhistograms
movielens movies movielens/movies histograms Percentile SSTables Write Latency Read Latency Partition Size Cell Count (micros) (micros) (bytes) 50% 1.00 0.00 545.79 149 7 75% 1.00 0.00 654.95 179 7 95% 1.00 0.00 654.95 215 8 98% 1.00 0.00 654.95 215 10 99% 1.00 0.00 654.95 258 10 Min 1.00 0.00 379.02 61 6 Max 1.00 0.00 654.95 310 12
32.
30 $ nodetool cfhistograms
movielens movies movielens/movies histograms Percentile SSTables Write Latency Read Latency Partition Size Cell Count (micros) (micros) (bytes) 50% 1.00 0.00 545.79 149 7 75% 1.00 0.00 654.95 179 7 95% 1.00 0.00 654.95 215 8 98% 1.00 0.00 654.95 215 10 99% 1.00 0.00 654.95 258 10 Min 1.00 0.00 379.02 61 6 Max 1.00 0.00 654.95 310 12
33.
250X
34.
compression = {'chunk_length_in_kb': '4', ‘class’: ‘org.apache.cassandra.io.compress.LZ4Compressor’}
35.
READ-AHEAD
36.
34 $ blockdev --report RO
RA SSZ BSZ StartSec Size Device ro 256 512 1024 0 91176960 /dev/loop0 ro 256 512 1024 0 87863296 /dev/loop1 ro 256 512 1024 0 91176960 /dev/loop2 rw 256 512 4096 0 6001175126016 /dev/sda
37.
34 $ blockdev --report RO
RA SSZ BSZ StartSec Size Device ro 256 512 1024 0 91176960 /dev/loop0 ro 256 512 1024 0 87863296 /dev/loop1 ro 256 512 1024 0 91176960 /dev/loop2 rw 256 512 4096 0 6001175126016 /dev/sda
38.
256K?
39.
REMINDER: 258 BYTES PER
PARTITION
40.
1000X
41.
TURN IT OFF
42.
320K PER SSTABLE ACCESS
43.
DISABLE DYNAMIC SNITCH
44.
41
45.
41
46.
COMPACTION
47.
TWCS (NEVER DTCS)
48.
44 Today Yesterday 2
Days Ago
49.
50.
ROW CACHE
51.
47 $ nodetool info ID
: b07fa110-7deb-49aa-a020-22e1540d0f5c Gossip active : true Native Transport active: true Load : 6.03 MiB Generation No : 1516820539 Uptime (seconds) : 586 Heap Memory (MB) : 67.40 / 4016.00 Off Heap Memory (MB) : 0.01 Data Center : datacenter1 Rack : rack1 Exceptions : 0 Key Cache : entries 26, size 2.35 KiB, capacity 100 MiB, 113 hits, 140 requests, 0.807 recent hit rate, 14400 save period in seconds Row Cache : entries 1, size 346 bytes, capacity 100 MiB, 1 hits, 2 requests, 0.500 recent hit rate, 0 save period in seconds Counter Cache : entries 0, size 0 bytes, capacity 50 MiB, 0 hits, 0 requests, NaN recent hit rate, 7200 save period in seconds Chunk Cache : entries 33, size 2.06 MiB, capacity 480 MiB, 47 misses, 264 requests, 0.822 recent hit rate, NaN microseconds miss latency Percent Repaired : 100.0% Token : (invoke with -T/--tokens to see all 256 tokens)
52.
47 $ nodetool info ID
: b07fa110-7deb-49aa-a020-22e1540d0f5c Gossip active : true Native Transport active: true Load : 6.03 MiB Generation No : 1516820539 Uptime (seconds) : 586 Heap Memory (MB) : 67.40 / 4016.00 Off Heap Memory (MB) : 0.01 Data Center : datacenter1 Rack : rack1 Exceptions : 0 Key Cache : entries 26, size 2.35 KiB, capacity 100 MiB, 113 hits, 140 requests, 0.807 recent hit rate, 14400 save period in seconds Row Cache : entries 1, size 346 bytes, capacity 100 MiB, 1 hits, 2 requests, 0.500 recent hit rate, 0 save period in seconds Counter Cache : entries 0, size 0 bytes, capacity 50 MiB, 0 hits, 0 requests, NaN recent hit rate, 7200 save period in seconds Chunk Cache : entries 33, size 2.06 MiB, capacity 480 MiB, 47 misses, 264 requests, 0.822 recent hit rate, NaN microseconds miss latency Percent Repaired : 100.0% Token : (invoke with -T/--tokens to see all 256 tokens)
53.
COUNTERS
54.
49 $ nodetool info … Counter
Cache : entries 1, size 112 bytes, capacity 50 MiB, 15 hits, 16 requests, 0.938 recent hit rate, 7200 save period in seconds …
55.
ACT 3: JVM
TUNING (CMS + PARNEW)
56.
51 Eden OldS0 S1
57.
51 Eden OldS0 S1
58.
51 Eden OldS0 S1
59.
51 Eden OldS0 S1
60.
COPYING / PROMOTION IS
SLOW
61.
WRITE HEAVY WORKLOADS
62.
READ HEAVY WORKLOADS
63.
QUESTIONS?
Download now