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Flash Memory technology, deployed as server-side PCIe or solid state disks (SSDs), is emerging as a critical tool for performance and efficiency in data centers of all scales. This presentation will discuss how the use of Flash impacts Cassandra deployments in terms of configuration, DRAM requirements and performance expectations. Ideas on leveraging C*'s cutting-edge data-center awareness to blend flash and disk storage nodes for cost and workload efficiency will also be shared. Flash media itself will be examined from a physical perspective to understand endurance issues. Data on write amplification under bulk-load and operational workload conditions will be presented to explain the impact to Flash of C*'s Log Structured Merge Tree architecture and the associated compactions. Finally, we will examine strategies to make Cassandra more Flash-aware using both conventional techniques as well as emerging Non-volatile memory (NVM) programming capabilities. Lessons learned from real-world customer deployments will be shared to complete this presentation.
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#CASSANDRA13
Ma#hew Stump | Architect @ KISSmetrics
Real-time Large Queries
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KISSmetrics Customers Want*Churn Prediction
*AB Tests
*Which Blog Posts and Ad Campaigns Attract High Value Customers?
*User Conversion Funnel
*Revenue Prediction
*Customer Acquisition Costs
*Customer Lifetime Value
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Understanding Queries
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RowKey username first_name last_name postal_code
cstar cstar Cassandra Database 94110
user2 user2 Some Guy 94112
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RowKey username first_name last_name postal_code
cstar cstar Cassandra Database 94110
user2 user2 Some Guy 94112
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RowKey username first_name last_name postal_code
cstar cstar Cassandra Database 94110
user2 user2 Some Guy 94112
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RowKey
94110 cstar
94112 user2 user4 user7 ...
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Where Secondary Indexes Break
Source: Place source content or footnote here. Delete if not needed.
*High Cardinality Data
*Only one index per query
*Indexes are distributed
*Only some datatypes; no counters
*Range queries are expensive
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What Do I Want?
Source: Place source content or footnote here. Delete if not needed.
*Index high cardinality data; e.g. counters
*Complex queries, with multiple clauses
*Results in < 500ms for billions of rows
*Sub-field searching with regular expressions
*Range queries
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Bitmap and Bit-Slice Indexes
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RowKey
94110 cstar
94112 user2 user4 user7 ...
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RowKey
94110 00001000 01000000 00000000 000000000
94112 10000110 01000000 00000000 000000000
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RowKey
94110 00001000 01000000 00000000 000000000
94112 10000110 01000000 00000000 000000000
hash(“cstar”) = 4
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SELECT * FROM users WHERE zipcode = 94110 OR zipcode = 94112
94112 or 94110
10001110 01000000 00000000 000000000
Field Index
94110 10001010 01000000 00000000 000000000
94112 10000110 01000000 00000000 000000000
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SELECT * FROM users WHERE Event1 = true AND Event2 = true
Event1 and Event2
10000010 01000000 00000000 000000000
Field Index
Event1 10001010 01000000 00000000 000000000
Event2 10000110 01000000 00000000 000000000
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Field Value Slice
event_counter 1 10001010 01000000 00000000 000000000
event_counter 2 10000110 01000000 00000000 000000000
SELECT * FROM users WHERE event_counter < 5
Value1 or Value2
10000010 01000000 00000000 000000000
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"this is a test string"
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['thi', 's i', 's a', ' te', 'st ', 'str', 'ing']
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['0x746869', '0x732069', '0x732061', '0x207465', '0x737420', '0x737472', '0x696e67']
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Field Value Slice
text_field 0x207465 ' te' 10001010 01000000 00000000 000000000
text_field 0x696e67 'ing' 10111110 10001000 00000000 000001000
text_field 0x732061 's a' 10001010 01000001 00001000 110101110
text_field 0x732069 's i' 10001010 01000000 10110011 000000000
text_field 0x737420 'st ' 10001010 01001100 10110111 000000000
text_field 0x737472 'str' 10001010 01000000 00011010 011000000
text_field 0x746869 'thi' 10001010 01000000 10110111 000000010
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"thi.*ing"
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"thi" AND "ing"
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0x746869 AND 0x696e67
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Field Value Slice
text_field 0x207465 ' te' 10001010 01000000 00000000 000000000
text_field 0x696e67 'ing' 10111110 10001000 00000000 000001000
text_field 0x732061 's a' 10001010 01000001 00001000 110101110
text_field 0x732069 's i' 10001010 01000000 10110011 000000000
text_field 0x737420 'st ' 10001010 01001100 10110111 000000000
text_field 0x737472 'str' 10001010 01000000 00011010 011000000
text_field 0x746869 'thi' 10001010 01000000 10110111 000000010
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"th.*ing"
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"th" AND "ing"
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range(0x746800, 0x7468FF) AND 0x696e67
range("th" + 0x00, "th" + 0xFF) AND "ing"
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Field Value Slice
text_field 0x207465 ' te' 10001010 01000000 00000000 000000000
text_field 0x696e67 'ing' 10111110 10001000 00000000 000001000
text_field 0x732061 's a' 10001010 01000001 00001000 110101110
text_field 0x732069 's i' 10001010 01000000 10110011 000000000
text_field 0x737420 'st ' 10001010 01001100 10110111 000000000
text_field 0x737472 'str' 10001010 01000000 00011010 011000000
text_field 0x746869 'thi' 10001010 01000000 10110111 000000010
text_field 0x74687A 'thz' 10000000 00000001 00011100 000110010
range(0x746800, 0x7468FF) AND 0x696e67
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Implementation
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Query & Indexing Engine
Queries and Events
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RowKey Offset 0x00 Offset 0x01 Offset 0x02 Offset 0x03
event1_0x00 10011000 10011000
event1_0x01 10011000 10011000 10011000
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Results So Far*Results returned for an 8 clause query for 4 billion rows < 2 second
*Full regular expression support
*Full support for range queries
*Ability to index any numeric value, or value which can be hashed.
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What isn't finished*Support for atomic counters
*"Group By" query aggregation
*Still working on event processing and distribution
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https://github.com/project-z/
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[email protected]@mattstump
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THANK YOU