Your SlideShare is downloading. ×
Phoenix h basemeetup
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Phoenix h basemeetup

1,048
views

Published on


0 Comments
3 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,048
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
36
Comments
0
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Demos: GOC demo – popups and filters Pulse – show Splunk dashboard and talk to the process – Shriman Stats.pl for GSI and SDA - Saran
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Add stories the team is planning to work on for the next sprint – List in priority order
  • Transcript

    • 1. PhoenixWe put the SQL back in the NoSQLJames Taylorjtaylor@salesforce.com
    • 2. AgendaPhoenix OverviewPhoenix ImplementationPerformance AnalysisPhoenix RoadmapDemo Completed
    • 3. Phoenix OverviewSQL layer on top of HBaseDelivered as a embedded JDBC driverTargeting low latency queries over HBase dataColumns modeled as multi-part row key and key valuesQuery engine transforms SQL into series of scansUsing native HBase APIs and capabilities Completed Coprocessors for aggregation Custom filters for expression evaluation Transaction isolation through scan time range Optionally client-controlled timestampsOpen sourcing soon100% Java
    • 4. Phoenix SQL Support SELECT <expression>… FROM <table> WHERE <expression> GROUP BY <expression>… HAVING <aggregate expression> ORDER BY <aggregate expression>… LIMIT <value>Aggregation Functions  MIN, MAX, AVG, SUM, COUNTBuilt-in Functions  SUBSTR, ROUND, TRUNC, TO_CHAR, TO_DATEOperators  =,!=,<>,<,<=,>,>=, LIKE  AND, OR, NOTBind Parameters  ?, :#CASE WHENIN (<value>…)DDL/DML (in progress)  CREATE/DROP <table>  DELETE FROM <table> WHERE <expression>  UPSERT INTO <table> [(<column>…)] VALUES (<value>…)
    • 5. Sample QueriesSELECT host, TRUNC(dateTime, DAY), Completed AVG(cache_hit), MIN(cache_hit), MAX(cache_hit)FROM server_metricsWHERE host LIKE cs11-%AND dateTime> TO_DATE(2012-04-01)AND dateTime< TO_DATE(2012-07-01)GROUP BY host, TRUNC(dateTime, DAY)HAVING MIN(cache_hit) < 90ORDER BY host, AVG(cache_hit)SELECT product_number, product_name, CASE WHEN list_price = 0 THEN Mfg item - not for resale WHEN list_price < 50 THEN Under $50 WHEN list_price >= 50 and list_price < 250 THEN Under $250 WHEN list_price >= 250 and list_price < 1000 THEN Under $1000 ELSE Over $1000 END as price_categoryFROM product_catalogueWHERE product_category IN (Camping, Hiking’)AND (product_name LIKE %Pack’ OR product_name LIKE % Cots %’)
    • 6. Query Processing Product Metrics HTable Row Key ORG_ID DATE FEATURE TXNS Key Values IO_TIME RESPONSE_TIME ScanSELECT feature, SUM(txns)  Start key: ORG_ID (:1) + DATE (:2)FROM product_metrics  End key: ORG_ID (:1) + DATE (:3) FilterWHERE org_id = :1  Filter: IO_TIME > 100AND date >= :2 AggregationAND date <= :3  Intercepts scan on region server  Builds map of distinct FEATURE valuesAND io_time > 100  Returns one row per distinct groupGROUP BY feature  Client does final merge
    • 7. Phoenix Query OptimizationsStart/stop key of scan based on AND-ed columns Through SUBSTR, ROUND, TRUNC, LIKEParallelized on client by chunking over start/stop key of scanAggregation on region-servers through coprocessor Inline for GROUP BY over row key ordered columns In memory map per group otherwiseWHERE clause executed through custom filters Completed Incremental evaluation with early termination Evaluated through byte pointersIN and OR over same column (in progress) Becomes batched get or filter with next row hintTop N queries (future) Through coprocessor keeping top N rowsTABLESAMPLE (future) Becomes filter with next row hint
    • 8. Phoenix Performance
    • 9. Phoenix Performance Completed
    • 10. Phoenix RoadmapIncrease breadth of SQL support DML/DDL (in progress) Derived tables (SELECT * FROM (SELECT foo FROM bar)) More built-in functions: COALESCE, UPPER, TRIM More operators: ||, IS NULL, *,/,+,-Secondary indexes Multiple projections for immutable data Reordered columns Completed in row key Different levels of aggregation Incrementally maintained for non immutable dataTABLESAMPLE for samplingImprove multi-byte supportJoins Hash joinOLAP extensions OVER PARTITION BY
    • 11. Demo CompletedTime-series database chartinghttp://goo.gl/61WRs
    • 12. Thank you!Questions/comments?