cassandra read/write paths

Cassandra Read / Write

John Sumsion from FamilySearch Utah Cassandra Meetup

13 Aug. 2015

Agenda

•  Rows vs ParGGons/Cells •  Kinds of OperaGons •  Datafile View •  Machine View •  New in Cassandra 3.0.0-‐alpha1

•  OLTP on 500M+ enGGes •  Course grain data model -‐ persons in pedigree

Agenda


Rows vs ParGGons/Cells


•  Example -‐ User / Movie raGngs – U1: M423: 4 stars, “I liked it”, 8/20/2011 – U1: M205: 1 stars, “Won’t go again”, 7/29/2011 – U2: M423: 3 stars, “Movie was ok”, 7/1/2013 – U3: M423: 5 stars, “My favorite”, 6/23/2014

•  The user id (U1) is the parGGon key •  The movie id (M423) is the clustering key •  All other values are not part of any key

Agenda


Kinds of OperaGons


•  Example -‐ User / Movie raGngs – U1 | M423; 8/20/2011 | 4 stars, “I liked it” – U1 | M205; 7/29/2011 | 1 stars, “Won’t go again” – U2 | M423; 7/1/2013 | 3 stars, “Movie was ok” – U3 | M423; 6/23/2014 | 5 stars, “My favorite”

•  ParGGon read (all values for U1) •  Range scan read (raGng from U1 for M243) •  Update cell (change raGng for U2, M423)

Agenda


Datafile View -‐ RDBMS

Datafile View -‐ Cassandra

Agenda

•  Rows vs ParGGons/Cells •  Kinds of OperaGons •  Datafile View -‐ Cassandra Writes •  Machine View •  New in Cassandra 3.0.0-‐alpha1

Datafile View -‐ Cassandra Writes


•  Three supported compacGon strategies: 1.  Size-‐Gered 2.  Fixed-‐size (leveled) 3.  Date-‐Gme

Datafile View -‐ Cassandra Writes Size-‐Tiered (STCS) Fixed-‐Size (or Leveled, LCS)

Datafile View -‐ Cassandra Writes Size-‐Tiered (STCS) Fixed-‐Size (or Leveled, LCS)

Date-‐Time


Date-‐Time

Fixed-‐Size (or Leveled, LCS) Size-‐Tiered (STCS)

Agenda


Machine View -‐ RDBMS

Many styles, someGmes combined: •  High verGcal scale, many-‐core, R/W •  Master serving R/W, slaves serving R •  Hot standby for machine failure

Each opGon is costly to engineer and build.

Machine View -‐ Cassandra

Agenda


New in Cassandra 3.0.0-‐alpha1

CASSANDRA-‐8099 •  New file format

opGmizaGons •  Like run-‐length

encoding for redundant data

•  More efficient to scan / read

Takeaways

•  Think parGGons / cells not rows •  Think scaier / gather, not direct read •  Think scale out & opGmize, not scale up

•  Core Cassandra read/write paths are gekng some serious aienGon and opGmizaGon

Data Model Ideas

•  Data model ideas for consistency? search “John Sumsion CassandraSummit 2014”

Wrap-‐up


•  John Sumsion •  Email: [email protected]

cassandra read/write paths

Software