Download

Joins on Encoded Data - KAIST

Upload
0
Embed
Share
Upload and view presentations on any device and embed the player to your website! --- > >
Upload PPT
Upload PPT

Download this Presentation

lamberto

2021-10-12 published at Spiritual/ Inspirational category

Presentation Transcript

1.Joins on Encoded and Partitioned DataJae-Gil Lee2* Gopi Attaluri3 Ronald Barber1 Naresh Chainani3 Oliver Draese3 Frederick Ho5 Stratos Idreos4* Min-Soo Kim6* Sam Lightstone3 Guy Lohman1 Konstantinos Morfonios8* Keshava Murthy10*Ippokratis Pandis7* Lin Qiao9* Vijayshankar Raman1 Vincent Kulandai Samy3 Richard Sidle1 Knut Stolze3 Liping Zhang31IBM Almaden Research Center 2KAIST, Korea 3IBM Software Group4Harvard University 5IBM Informix 6DGIST, Korea 7Cloudera 8Oracle 9LinkedIn 10MapR* Work was done while the author was with IBM Almaden Research Center VLDB 2014 Industrial Track
2.Table of Contents Introduction Partitioning Column Domains Encoding Join Columns Encoding Non-Join Columns Experiment Results Conclusions
3.Blink Project Accelerator technology developed by IBM Almaden Research Center since 2007 Main features Storing a compressed copy of a (portion of a) data warehouse Exploiting (i) large main memories, (ii) commodity multi-core processors, and (iii) proprietary compression Improving the performance of typical business intelligence(BI) SQL queries by 10 to 100 times Not requiring the tuning of indexes, materialized views, etc. Products offered by IBM based upon Blink Informix Warehouse Accelerator: released on March 2011 IBM Smart Analytics Optimizer for DB2 for z/OS V1.1 A predecessor to today’s IBM DB2 Analytics Accelerator for DB2 for z/OS
4.Informix Warehouse Accelerator(IWA) A main-memory accelerator to the disk-based Informix database server product, packaged as the Informix Ultimate Warehouse Edition(IUWE) System Architecture Data Loading and Query Execution
5.Main Features Related to Joins Performing joins directly on encoded data Join method: hash joins Encoding method: dictionary encoding Handling join columns encoded differently: encoding translation Partitioning a column to support incremental updates and achieve better compression: frequency partitioning Encoding non-join(payload) columns on the fly
6.Hash Joins Build phase Scan each dimension table, applying local predicates Hash to an empty bucket in the hash table Store the values of join columns as well as “payload” columns Probe phase Scan the fact table, applying local predicates Look up the hash table with the foreign key per dimension Retrieve the values of payload columns Example A simple join query betweenLINEITEM and ORDERS scan(ORDERS) σ(O_OrderDate …) scan(LINEITEM) σ(L_ShipDate …) σ(L_OrderKey IN …) Look up the values of O_OrderDate Group by, Aggregation O_OrderKey O_OrderDate Dimension Fact Hash Table
7.Dictionary Encoding A value of a column is replaced by an encoded value requiring only a few bits Example Dictionary Encoding 10bytes 6bits
8.Table of Contents Introduction Partitioning Column Domains Encoding Join Columns Encoding Non-Join Columns Experiment Results Conclusions
9.Updates in Dictionary Encoding Option 1: leaving room for future values Downside: overestimation of the number of future values will waste bits; underestimation will require re-encoding all values to add additional ones beyond the capacity Option 2: partitioning the domain and creating separate dictionaries for each partition  our approach Upside: the impact of adding new values can be isolated from the dictionaries of any existing partitions New values are simply added to a partition that will be created on the fly, as values arrive We leave the values in that partition unencoded
10.Frequency Partitioning Achieving better compression: approximate Huffman Defining fixed-length codes within a partition Example Top 64 traded goods –6 bit code Rest origin product ChinaUSA GER,FRA,… Rest Column partitions Cell 4 Cell 1 Cell 2 Cell 3 Cell 5 Cell 6 Sales China, USA: 1bit EU: 5bits Rest: 8bits 1M, 100K, 10K occurrences of each group Frequency partitioning= 8bits for all countries= 1.58Mbits 8.88Mbits
11.Catch-All Cell (1/2) Cell: an intersection of the partitions for each column The rows having one of the values from each corresponding partition, where each row is formed by concatenating the fixed-length code for each of its columns Potential problem: proliferation of cells e.g., 2 partitions for each column (one for encoded, one for unencoded)  2 𝐶 , 𝐶 is the number of columns Catch-all cell: a special cell for unencoded values Any rows containing an unencoded value in any column Benefit: minimizing the number of cells for unencoded values
12.Catch-All Cell (2/2) Example Containing the 5th and 6th rows in unencoded form LINEITEM Encoding 100 200 100 300 100 400 8/2/2010 9/4/2010 9/4/2010 8/2/2010 5/1/2010 8/2/2010 Cell 0: K0 X D0 Cell 1: K1 X D0 Catch-All Cell 0 0 0 1 0 1 1 0 100 400 5/1/2010 8/2/2010 Dictionary of LINEITEM L_OrderKey Partition K0: 100 Partition K1: 200 300 L_ShipDate Partition D0: 8/2/2010 9/4/2010 L_OrderKey L_ShipDate L_OrderKey L_ShipDate unencodable same value
13.Table of Contents Introduction Partitioning Column Domains Encoding Join Columns Encoding Non-Join Columns Experiment Results Conclusions
14.Joins on Encoded Values (1/2) Option 1: per-domain encoding Encoding join columns identically on disk 𝑉 1 = 𝑉 2 ⟺𝑀 𝑉 1 =𝑀( 𝑉 2 ), 𝑀 is an encoding scheme Not clear which column’s distribution should be picked up Option 2: translation to common code Translating both join columns to a new common encoding at runtime Incurring the CPU cost of decoding and re-encoding both columns ⊳⊲ ⊳⊲ ⊳⊲ Encoded using the same scheme
15.Joins on Encoded Values (2/2) Option 3: per-column encoding  our approach Encoding join columns independently on disk Translating only one join column to the encoding of the other at runtime Encoding translation: 𝑀 𝐹𝐾 ( 𝑀 𝑃𝐾 −1 𝐸 𝑃𝐾 ) Typically, translating from the encoding of the build side to the encoding of the probe side ⊳⊲ ⊳⊲ Encoding Translation build probe build probe
16.Advantages of Per-Column Encoding Better compression The ideal encoding for one column may not be ideal for the other (see next page) Flexible reorganization Any tables sharing a common dictionary are inextricably linked Ad hoc querying Which columns might be joined in a query may not be known when the data is encoded
17.Better Compression of Skewed Data 33~50% gain 21% gain per-column per-domain
18.Encoding Translation Challenge Dealing with the multiple representations of the same value caused by the catch-all cell At least, one encoded and one unencoded Two variants DTRANS(Dimension TRANSlation) Resolving the multiple representations in the dimension-table scan Reducing the overhead of the probe phase FTRANS(Fact TRANSlation) Resolving the multiple representations during the fact-table scan Reducing the overhead of the build phase
19.Encoding Translation: DTRANS Partition 0 Partition 1 Catch-All Cell 0 0 0 1 100 400 HT[0] HT[1] HT[2] 0 0 1 100 200 300 400 Hash Tables Direct Probes Data ORDERS O_OrderKey O_OrderStatus "S" "S" "S" "S" "R" 100 200 300 400 500 0 0 1 100 200 300 400 Hash Tables HT[0] HT[1] HT[2] Build Phase: Probe Phase: Having all qualifying key values in unencoded form 1 hash table per fact-table partition Encodable Unencodable
20.Encoding Translation: FTRANS Partition 0 Partition 1 Catch-All Cell 0 0 0 1 100 400 0 Fail: 400 Data 0 0 1 400 Hash Tables HT[0] HT[1] HT[2] Encoding ORDERS "S" "S" "S" "S" "R" 100 200 300 400 500 0 0 1 400 Hash Tables HT[0] HT[1] HT[2] O_OrderKey O_OrderStatus Build Phase: Probe Phase: Testing encodability Having only unencodable key values 1 hash table per fact-table partition Encodable Unencodable
21.Table of Contents Introduction Partitioning Column Domains Encoding Join Columns Encoding Non-Join Columns Experiment Results Conclusions
22.On-the-Fly(OTF) Encoding (1/2) Reasons for encoding payload columns The join key is usually just an integer, whereas the payloads are often wider strings  higher impact of compression Benefits of the on-the-fly(OTF) encoding Updates: a mixture of encoded and unencoded payloads are hard to maintain using hash tables Expressions: the results of an expression, e.g., MONTH(ShipDate), can be encoded very compactly Correlation: correlated columns in a query, e.g., City, State, ZIPCode, and Country, can be used to create a tighter code Predicates: local/join predicates will likely reduce the cardinality of each column, allowing a more compact representation
23.On-the-Fly(OTF) Encoding (2/2) Mechanism Use a mapping table that consists of a list of hash tables Return an index into the bucket where the value was inserted  an OTF code The OTF code is not changed, even if the hash table is resized Example 600+1024+2048+40=3712 Size: 1024 Size: 2048 Size: 4096 Hash Tables 40 value Original Dictionary Size: 600
24.Table of Contents Introduction Partitioning Column Domains Encoding Join Columns Encoding Non-Join Columns Experiment Results Conclusions
25.Experimental Setting Five alternative configurations Data set and queries: a simplified TPC-H data set and queries Measure: time for (i) build phase, (ii) probe phase, and (iii) scan 𝑡 𝑏𝑢𝑖𝑙𝑑 𝑡 𝑝𝑟𝑜𝑏𝑒 𝑡 𝑏𝑎𝑠𝑒
26.Per-Domain vs. Per-Column DTRANS(per-column) outperforms: DECODE in query performance 1DICT(per-domain) in compression ratio
27.When Does DTRANS Win? wall clock time (sec) DTRANS outperforms FTRANS when: Dimension tables are small , OR High ratio of rows are left unencoded Varying the dimension size Varying the ratio of unencoded rows
28.Summary of the Results DTRANS or FTRANS outperform traditional DECODE for most cases by up to 40% of query performance DTRANS or FTRANS improve the compression ratio by at least 16%(or up to 50% in skewed data), with negligible overhead in query processing, in comparison with having one dictionary for both join columns(1DICT) DTRANS is preferred when dimension tables are small FTRANS is preferred when a fact table is small or local predicates on a fact table are very selective DTRANS is preferred when high ratio of unencoded rows
29.Table of Contents Introduction Partitioning Column Domains Encoding Join Columns Encoding Non-Join Columns Experiment Results Conclusions
30.Conclusions Partitioning column domains benefits: Compression ratio (partition by frequency) Incremental update without changing dictionaries Independently encoding join columns: Optimizes compression of each Requires translation at run time Translating dimension table's values preferred when | Dimension table | ≪ | Fact table |, OR High ratio of unencoded rows Encoding payload columns on the fly reduces hash-table space Implemented in Informix Warehouse Accelerator
31.Blink Refereed Publications Jae-Gil Lee et al.: Joins on Encoded and Partitioned Data. PVLDB 7(13): 1355-1366 (2014) Vijayshankar Raman et al.: DB2 with BLU Acceleration: So Much More than Just a Column Store. PVLDB 6(11): 1080-1091 (2013) Lin Qiao, Vijayshankar Raman, Frederick Reiss, Peter J. Haas, Guy M. Lohman: Main-memory scan sharing for multi-core CPUs. PVLDB 1(1): 610-621 (2008) Ryan Johnson, Vijayshankar Raman, Richard Sidle, Garret Swart: Row-wise parallel predicate evaluation. PVLDB 1(1): 622-634 (2008) Vijayshankar Raman, Garret Swart, Lin Qiao, Frederick Reiss, Vijay Dialani, Donald Kossmann, Inderpal Narang, Richard Sidle: Constant-Time Query Processing. ICDE 2008: 60-69 Allison L. Holloway, Vijayshankar Raman, Garret Swart, David J. DeWitt: How to barter bits for chronons: compression and bandwidth trade offs for database scans. SIGMOD Conference 2007: 389-400 Vijayshankar Raman, Garret Swart: How to Wring a Table Dry: Entropy Compression of Relations and Querying of Compressed Relations. VLDB 2006: 858-869
32.Thank You!Any Questions?

Latest Release