=Paper=
{{Paper
|id=None
|storemode=property
|title=Generalized Parallel Join Algorithms and Designing Cost Models
|pdfUrl=https://ceur-ws.org/Vol-899/paper5.pdf
|volume=Vol-899
|dblpUrl=https://dblp.org/rec/conf/syrcodis/Pigul12
}}
==Generalized Parallel Join Algorithms and Designing Cost Models==
https://ceur-ws.org/Vol-899/paper5.pdf
Generalized Parallel Join Algorithms and Designing Cost
Models
Alice Pigul
SPbSU
m05pay@math.spbu.ru
Abstract some discussion of experiments will be given.
Applications for large-scale data analysis use such 2 Related work
techniques as parallel DBMS, MapReduce (MR)
paradigm, and columnar storage. In this paper we focus
2.1 Architectural Approaches
in a MapReduce environment. The aim of this work is
to compare the different join algorithms and designing Column storage is one of the architectural approaches to
cost models for further use in the query optimizer. store data in columns, that the values of one field are
stored physically together in a compact storage area.
Column storage strategy improves performance by
1 Introduction reducing the amount of unnecessary data from disk by
excluding the columns that are not needed. Additional
Data-intensive applications include large-scale data gains may be obtained using data compression. Storage
warehouse systems, cloud computing, data-intensive method in columns outperforms row-based storage for
analysis. These applications have their own specific workloads typical for analytical applications, which are
computational workload. For example, analytic systems characterized by heavy selection operation from
produce relatively rare updates but heavy select millions of records, often with aggregation and by
operation with millions of records to be processed, often infrequent update operation. For this class of workloads
with aggregations. I/O is major factor limited the performance.
There are the following architectures that are used Comparison of column-wise and row-wise stores
to analyze massive amounts of data: MapReduce approaches is presented in [1].
paradigm, parallel DBMSs, column-wise store, and Another architectural approach is a software
various combinations of these approaches. framework MapReduce. Paradigm MapReduce was
Applications of this type process multiple data sets. introduced in [11] to process massive amounts of
This implies need to perform several join operation. It’s unstructured data.
known join operation is one of the most expensive Originally, this approach was contrasted with a
operations in terms both I / O and CPU costs. parallel DBMS. Deep analysis of the advantages and
Unfortunately, join algorithms is not directly disadvantages of these two architectures was presented
supported in MapReduce. There are some approaches to in [25,10].
solve this problem by using a high-level language Later, hybrid systems appeared in [9, 2]. There are
PigLatin, HiveQL for SQL queries or implementing three ways to combine approaches MapReduce and
algorithms from research papers. The aim of this work parallel DBMS.
is to generalize and compare existing equi-join MapReduce inside a parallel DBMS. The main
algorithms with some optimization techniques and build intention is to move computation closer to
cost model which could be used in a query optimizer for data. This architecture can be exemplified with
a distributed DBMS with MapReduce. hybrid database Greenplum with MAD
This paper is organized as follows the section 2 approach [9].
describe state of the art. Join algorithms and some
DBMS inside MapReduce. The basic idea is
optimization techniques were introduced in 3 section.
to connect multiple single node database
The designing of cost models for join algorithms are
systems using MapReduce as the task
presented in 4 section. Performance evaluation will be
coordinator and network communication layer.
described in 5 section. Finally, future direction and
An example is a hybrid database HadoopDB
[2].
Proceedings of the Spring Young Researcher's
Colloquium On Database and Information Systems MapReduce aside of the parallel DBMS.
SYRCoDIS, Moscow, Russia, 2012 MapReduce is used to implement an ETL
produced data to be stored in parallel DBMS.
This approach is discussed in [28] Vertica, the I/O cost for each phase separately is given in [24].
which also supports the column-wise store. Simple theoretical considerations for selecting a
Another group of hybrid systems combines particular join algorithm are presented in [21]. Another
MapReduce with column-wise store. MapReduce and approach [7] for selecting join algorithm is to measure
column-wise store are effective in data-intensive the correlation between the input size and the join
applications. Hybrid systems based on this two algorithm execution time with fixed cluster
techniques may be found in [20,13]. configuration settings.
2.2 Algorithms for Join Operation 3 Join algorithms and optimization
Detailed comparison of relational join algorithms was techniques
presented in [26]. In our paper, the consideration is
restricted to a comparison of joins in the context of In this section we consider various techniques of two-
MapReduce paradigm. way joins in MapReduce framework. Join algorithms
Papers which discuss equi-join algorithms can be can be divided into two groups: Reduce-side join and
divided into two categories which describe join Map-side join. The pseudo code presented in Listings,
algorithms and multi join execution plans. where R – right dataset, L – left dataset, V – line from
The former category deals with design and analyses join file, Key – join key, that was parsed from a tuple, in this
algorithm of two data sets. A comparative analysis of context tuple is V.
two-way join techniques is presented in [6, 4, 21]. The
cost model for two-way join algorithms in terms of cost 3.1 Reduce-Side join
I/O is presented in [7, 17].
The basic idea of multi-way join is to find strategies Reduce-side join is an algorithm which performs data
to combine the natural join of several relations. pre-processing in Map phase, and direct join is done
Different join algorithms from relation algebra are during the Reduce phase. Join of this type is the most
presented in [30]. The authors introduce the extension general without any restriction on the data. Reduce-side
of MapReduce to facilitate implement relation join is the most time-consuming, because it contains an
operations. Several optimizations for multi-way join are additional phase and transmits data over the network
described in [3, 18]. Authors introduced a one-to-many from one phase to another. In addition, the algorithm
shuffling strategy. Multi-way join optimization for has to pass information about source of data through the
column-wise store is considered in [20, 32]. network. The main objective of the improvement is to
Theta-Joins and set-similarity joins using reduce the data transmission over the network from the
MapReduce are addressed in [23] and [27] respectively. Map task to the Reduce task by filtering the original
data through semi-joins. Another disadvantage of this
2.3 Optimization techniques and cost models class of algorithms is the sensitivity to the data skew,
which can be addressed by replacing the default hash
In contrast to the sql queries in parallel database, the partitioner with a range partitioner.
MapReduce program contains user-defined map and There are three algorithms in this group:
reduce functions. Map and reduce functions can be General reducer-side join,
considered as a black-box, when nothing is known
Optimized reducer-side join,
about these functions, or they can be written on sql-like
languages, such as HiveQL, PigLatin, MRQL, or sql the Hybrid Hadoop join.
operations can be extracted from functions on semantic General reducer-side join is the simplest one. The
basis. Automatic finding good configuration settings for same algorithms are called Standard Repartition Join in
arbitrary program offered in [16]. Theoretical designing [6]. The abbreviation is GRSJ.
cost models for arbitrary MR program for each phase
Map (K: null, V from R or L)
separately presented in [15]. If the MR program is
Tag = bit from name of R or L;
similar to the semantics of SQL, it allows us to emit (Key, pair(V,Tag));
construct a more accurate cost model or adapt some of
the optimization techniques from relational databases. Reduce (K’: join key, LV: list of V with key K’)
HadoopToSQL [22] allows to take advantage of two create buffers Br and Bl for R and L;
different data storages such as SQL database and the for t in LV do
text format in MapReduce storage and to use index at add t.v to Br or Bl by t.Tag;
right time by transforming the MR program to SQL. for r in Br do
Manimal system [17] uses static analysis for detection for l in Bl do
emit (null, tuple(r.V,l.V));
and exploiting selection, projection and data
compression in MR programs and if needed to employ Listing 1: GRSJ.
B+ tree index. This algorithm has both Map and Reduce phases. In the
New SQL-like query language and algebra is presented Map phase, data are read from two sources and tags are
in [12]. But they are needed cost model based on attached to the value to identify the source of a
statistic. Detailed construction of the model to estimate key/value pair. As the key is not effecting by this
tagging, so we can use the standard hash partitioner. In set is pulled out of blocks from a distributed system in
Reduce phase, data with the same key and different tags the Reduce phase, where it is joined with another data
are joined with nested-loop algorithm. The problems of set that came from the Map phase. The similarity with
this approach are that the reducer should have sufficient the Map-side join is the restriction that one of the sets
memory for all records with a same key; and the has to be split in advance with the same partitioner,
algorithm sensitivity to the data skew. which will split the second set. Unlike Map-side join, it
Optimized reducer-side join enhances previous is necessary to split in advance only one set. The
algorithm by overriding sorting and grouping by the similarity with the Reduce-side join is that algorithm
key, as well as tagging data source. Also known as requires two phases, one of them for pre-processing of
Improved Repartition Join in [6], Default join in [14]. data and one for direct join. In contrast with the
The abbreviation is ORSJ. In the algorithm all the Reduce-side join we do not need additional information
values of the first tag are followed by the values of the about the source of data, as they come to the Reducer at
second one. In contrast with the General reducer-side a time.
join, the tag is attached to both a key and a value. Due
to the fact that the tag is attached to a key, the 3.2 Map-Side join
partitioner must be overridden in order to split the nodes Map-side join is an algorithm without Reduce phase.
by the key only. This case requires buffering for only This kind of join can be divided into two groups. First
one of input sets. Optimized reducer-side join inherits of them is partition join, when data previously
major disadvantages of General reducer-side join partitioned into the same number of parts with the same
namely the transferring through the network additional partitioner. The relevant parts will be joined during the
information about the source and the algorithm Map phase. This map-side join is sensitive to the data
sensitivity to the data skew. skew. The second is in memory join, when the smaller
Map (K:null, V from R or L) dataset send whole to all mappers and bigger dataset is
Tag = bit from name of R or L; partitioned over the mappers. The problem with this
emit (pair(Key,Tag), pair(V,Tag)); type of join occurs when the smaller of the sets can not
fit in memory.
Partitioner(K:key, V:value, P:the number of reducers) There are three methods to avoid this problem:
return hash_f(K.Key) mod P; JDBM-based map join,
Reduce (K’: join key, LV: list of V’ with key K’) Multi-phase map join,
create buffers Br for R; Reversed map join.
for t in LV with t.Tag corresponds to R do
add t.v to Br; Map-side partition join algorithm assumes that the two
for l in LV with l.Tag corresponds to L do sets of data pre-partitioned into the same number of
for r in Br do splits by the same partitioner. Also known as default
emit (null, tuple(r.V,l.V)); map join. The abbreviation is MSPJ. At the Map phase
Listing 2: ORSJ. one of the sets is read and loaded into the hash table,
The Hybrid join [4] combines the Map-side and then two sets are joined by the hash table. This
Reduce-side joins. The abbreviation is algorithm buffers all records with the same keys in
HYB. memory, as is the case with skew data may fail due to
lack of enough memory.
Job 1: partition the smaller file S
Map (K:null, V from S) Job 1: partition dataset S as in HYB
emit (Key,V); Job 2: partition dataset B as in HYB
Job 3: join two datasets
Reduce (K’:join key, LV: list of V’ with key K’) init() //for Map phase
for t in LV do read needed partition of output file from Job 1;
emit (null, t); add it to hashMap(Key, list(V)) H;
Map(K:null, V from B)
Job 2: join two datasets if (K in H) then
Map (K:null, V from B) for r in LV do
emit (Key,V); for l in H.get(K) do
emit(null, tuple(r,l));
init() //for Reduce phase Listing 4: MSPJ.
read needed partition of output file from Job 1; Map-side partition merge join is an improvement of the
add it to hashMap(Key, list(V)) H;
previous version of the join. The abbreviation is
Reduce (K’:join key, LV: list of V’ with key K’)
if(K’ in H) then
MSPMJ. If data sets in addition to their partition are
for r in LV do sorted by the same ordering, we apply merge join. The
for l in H.get(K’) do advantage of this approach is that the reading of the
emit (null, tuple(r,l)); second set is on-demand, but not completely, thus
memory overflow can be avoided. As in the previous
Listing 3: HYB.
cases, for optimization can be used the semi-join
In Map phase, we process only one set and the
filtering and range partitioner.
second set is partitioned in advance. The pre-partitioned
Job 1: partition S dataset as in HYB Idea of Reversed map join [21] approach is that the
Job 2: partition B dataset as in HYB bigger of the sets, which is partitions during the Map
Job 3: join two datasets phase, loading in the hash table. Also known as
init() //for Map phase Broadcast Join in [6]. The abbreviation is REV. The
find needed partition SP of output file from Job 1; second dataset is read from a file line by line and joined
read first lines with the same key K2 from SP and add using a hash table.
to buffer B;
Map(K:null, V from B) init() //for Map phase
while (K > K2) do read S from HDFS;
read T from SP with key K2; add it to hashMap(Key, list(V)) H;
while (K == K2) do map (K:null, V from S)
add T to B; add to hashMap(Key, V) H;
read T from SP with key K2; close() //for Map phase
if (K == K2) then find B in HDFS
for r in B do while (not end B) do
emit(null, tuple(r,V)); read line T;
K = join key from tuple T;
Listing 5: MSPMJ. if (K in H) then
In-Memory Join does not require to distribute original for l in H.get(K) do
data in advance unlike the versions of map joins emit(null, tuple(T,l));
discussed above. The same algorithms are called Map- Listing 7: REV.
side replication join in [7], Broadcast Join in [6],
Memory-backed joins [4], Fragment-Replicate join in 3.3 Semi-Join
[14]. The abbreviation is IMMJ. Nevertheless, this
algorithm has a strong restriction on the size of one of Sometimes a large portion of the data set does not take
the sets: it must fit completely in memory. The part in the join. Deleting of tuples that will not be used
advantage of this approach is its resistance to the data in join significantly reduces the amount of data
skew because it sequentially reads the same number of transferred over the network and the size of the dataset
tuples at each node. There are two options for for the join. This preprocessing can be carried out using
transferring the smaller of the sets: semi-joins by selection or by a bitwise filter. However,
using a distributed cache, these filtering techniques introduce some cost (an
reading from a distributed file system. additional MR job), so the semi-join can improve the
init() // for Map phase performance of the system only if the join key has low
read S from HDFS; selectivity. There are three ways to implement the semi-
add it to hashMap(Key, list(V)) H; join operation:
map (K:null, V from B) a semi-join using bloom-filter,
if (K in H) then
for l in H.get(K) do semi-join using selection,
emit (null, tuple(v,l));
an adaptive semi-join.
Listing 5: IMMJ.
The next three algorithms optimize the In-Memory Join Bloom-filter is a bit array that defines a membership of
for a case, when two sets are large and no of them fits element in the set. False positive answers are possible,
into the memory. but there are no false-negative responses in the solution
JDBM-based map join is presented in [21]. In this case, of the containment problem. The accuracy of the
JDBM library automatically swaps hash table from containment problem solution depends on the size of
memory to disk. the bitmap and on the number of elements in the set.
These parameters are set by the user. It is known that
The same as IMMJ, but H is implemented by HTree for a bitmap of fixed size m and for the data set of n
instead of hashMap . tuples, the optimal number of hash functions is
Listing 6: JDBM. k=0.6931*m/n. In the context of MapReduce, the semi-
Multi-phase map join [21] is algorithm where the join is performed in two jobs. The first job consists of
smaller of the sets is partitioned into parts that fit into the Map phase, in which keys from one set are selected
memory, and for each part runs In-Memory join. The and added to the Bloom-filter. The Reduce phase
problem with this approach is that it has a poor combines several Bloom-filters from first phase into
performance. If the size of the set, which to be put in one. The second job consists only of the Map phase,
the memory is increased twice, the execution time of which filters the second data set with a Bloom-filter
this join is also doubled. It is important to note that the constructed in previous job. The accuracy of this
set, which will not be loaded into memory, will be read approach can be improved by increasing the size of the
many times from the disk. bitmap. However in this case, a larger bitmap consumes
For part P from S that fit into memory do IMMJ(P,B). more amounts of memory. The advantage of this
method is its the compactness. The performance of the
Listing 7: Multi-phase map join. semi-join using Bloom-filter highly depends on the
balance between the Bloom-filter size, which increases
the time needed for its reconstruction of the filter in the The disadvantage of this approach is that additional
second job, and the number of false positive responses information about the source of data is transmitted over
in the containment solution. The large size of the data the network.
set can seriously degrade the performance of the join. Job 1: find keys which are present in two datasets
Job 1: construct Bloom filter Map (K:null, V from R or L)
Map (K:null, V from L) Tag = bit from name of R or L;
Add Key to BloomFilter Bl emit (Key,Tag);
close() //for Map phase
emit(null, Bl); Reduce (K’: join key, LV: list of V with key K’)
Val = first value from LV;
Reduce (K’: key, LV) //only 1 Reducer for t in LV do
for l in LV do if (not Val==Val2) then
union filters by operation Or emit (null, K’);
close() // for Reduce phase
write resulting filter into file; Job 2: before joining it is necessary to filter the smaller
dataset by keys from the Job 1 that will be loaded into
Job 2: filter dataset hash map. Then the bigger dataset is joined with filtered
init() //for Map phase one.
read filter from file in Bl
Listing 8: Adaptive semi-join.
Map (K:null, V from R)
if (Key in Bl) then
emit (null, V); 3.4 Range Partitioners
Job 3: do join with L dataset and filtered dataset from All algorithms, except the In-Memory join and their
Job 2. optimizations are sensitive to the data skew. This
Listing 7: Semi-join using Bloom-filter. section describes two techniques of the default hash
Semi-join with selection extracts unique keys and partitioner replacement.
constructs a hash table. The second set is filtered by the A Simple Range-based Partitioner [4] (this kind similar
hash table constructed in the previous step. In the to the Skew join in [14]) applies a range vector of
context of MapReduce, the semi-join is performed in dimension n constructed from the join keys before
two jobs. Unique keys are selected during the Map starting a MR job. By this vector join keys will be
phase of the first job and then they are combined into splitted into n parts, where n is the number of Reduce
one file during the Map phase. The second job consists jobs. Ideally partitioner vector is constructed from the
of only the Map phase, which filters out the second set. whole original set of keys, in practice a certain number
The semi-join using selection has some limitations. of keys is chosen randomly from the data set. It is
Hash table in memory, based on records of unique keys, known that the optimal number of keys for the vector
can be very large, and depends on the key size and the construction is equal to the square root of the total
number of different keys. number of tuples. With a heavy data skew into a single
key value, some elements of the vector may be
Job 1: find unique keys
Map (K:null, V from L)
identical. If the key belongs to multiple nodes, a node is
Create HashMap H; selected randomly in the case of data on which to build
if (not Key in H) then a hash table, otherwise the key is sent to all nodes (to
add Key to H; save memory as a hash table is contained in the
emit (Key, null); memory).
Virtual Processor Partitioner [4] is an improvement of
Reduce (K’: key, LV) //only one Reducer the previous algorithm based on increasing the number
emit (null,key); of partition. The number of parts is specified multiple of
the tasks number. The approach tends to load the nodes
Job 2: filter dataset
with the same keys uniformly (compared with the
init() //for Map phase
add to HashMap H unique keys from job 1; previous version). The same keys are scattered on more
Map (K:null, V from R) nodes than in the previous case.
if (Key in H) then
emit (null,V);
Job 3: do join with L dataset and filtered dataset from
Job 2.
Listing 8: Semi-join with selection.
The Adaptive semijoin is performed in one job, but
filters the original data on the flight during the join.
Similar to the Reduce-side join at the Map phase the
keys from two data sets are read and values are set
equal to tags which identify the source of the keys. At
the Reduce phase keys with different tags are selected.
//before the MR job starts behavior of each algorithm for parallel query.
// optimal max = sqrt(|R|+|L|) Analytical model is cost formulas that are used to
getSamples (Red:the number of reducers, max: the max calculate the query execution time, taking into account
number of samples) the specific of parallel algorithm. Below, analytical
C = max/Splits.length; cost model for join algorithms and their optimizations
Create buffer B; will be constructed.
for s in Splits of R and L do
get C keys from s;
4.1Configuration settings
add it to B;
sort B;
//in case simple range partitioner P == 1 Execution of MR program depends on input data
//in case virtual range partitioner P > 1 statistic such as selectivity, skew, compression, on
for j<(Red*P) do cluster resource such as number of nodes, on
T = B.length/(Red*P)*(j+1); configuration parameters, such as I/O cost, and on
write into file B[T]; properties of specific algorithm. Below, the parameters
used in the analysis are presented in table.
Map(K:null, V from L or R)
Tag = bit from name of R or L;
Variable Description
read file with samples and add samples to Buffer B;
//in case virtual partition it is needed to s(x) Size of x in mb
// each index mod |Reducers| p(x) Number of pairs for split x
Ind = {i: B[i-1] < Key <= B[i]} wid Pair width
// Ind may be array of indexes in skew case ct The average computation time needed per
if (Ind.length >1) then pair
if (V in L) then pC The cost for partition
node = random(Ind);
emit (pair(Key, node), pair(V, Tag));
sC The cost for serialization
else sortC The cost for sorting on keys
for i in Ind do cC The cost for executing combine function
emit (pair(Key, i), pair(V, Tag)); mC The cost for merge
else selP Selectivity of pairs
emit (pair(Key, Ind), pair(V, Tag)); selC Selectivity of combining
|red| Number of reducers
Partitioner (K:key, V:value, P:the number of reducers)
return K.Ind; |map| Number of mappers
rh The cost for reading from HDFS
Reducer (K’: join key, LV: list of V’ with key K’) wh The cost for writing to HDFS
The same as GRSJ rwl The cost for local I/O operations
Listing 8: The range partitioners. tC The cost of network transfer
sortMB io.sort.mb parameter in Hadoop
3.5 Distributed cache configuration
sortRP io.sort.record.percent
The advantage of using distributed cache is that data set sortSP io.sort.spill.percent
are copied only once at the node. It is especially F io.sort.factor
effective if several tasks at one node need the same file. shuBP mapred.job.shuffle.input.buffer.percent
In contrast the access to the global file system needs shuMP mapred.job.shuffle.merge.percent
more communication between the nodes. Better memMT mapred.inmem.merge.threshold
performance of the joins without the cache can be memT mapred.child.java.opts
achieved by increasing number of the files replication, redBP mapred.job.reduce.input.buffer.percent
so there's a good chance to access the file version
locally. 4.2 Cost of arbitrary MR program
4 Cost model As mentioned above, the MR job consists of the
execution stages, thus it is possible to estimate each
Due to significant differences between parallel DBMS phase separately. Job may contain the following stages:
and MapReduce, the MapReduce paradigm requires Setup, Read (read map input), Map (map function),
another optimization techniques based on indexing and Buffer (serializing to buffer, partitioning, sorting,
compression, programming models, data distribution combining, compressing, write output data to local
and query execution strategy. Therefore, we need a disk), Merge (merging spill files), Shuffle (transferring
different strategy of designing model cost. There are map output to reducers), MergeR(merging received
two types of designing cost models: the task execution files), Reduce (reduce function), Write (writing result to
simulation [29] and analytical cost calculation [15, 24]. the HDFS), Cleanup. Due to the fact that the job of MR
To measure the query parallelism effectiveness, it is program carried out in parallel or in waves, it is possible
need to build a cost model that can describe the to calculate the approximate total cost of the job
through the cost of one task (one mapper and one The number of spill files equal to sum of spill files at
reducer). The Cost job take into account the parallel first pass (S1P), at intermediate pass (SIP) and at final
pass (SFP):
threads of execution and compute the total cost of MR
job, where cm and cr are costs of one task mapper or N , N F
reducer respectively, MaxMN and MaxRN are S1 F , ( N 1) mod( F 1) 0,
maximum map tasks or reduce task per node. ( N 1) mod( F 1) 1
| map | *c m | red | *c r
Cost job ctr 0, N F
| nodes | *MaxMN | nodes | *MaxRN
This formula is bad for the skew data, when one task is SIP N S1
S1 F * F , N F
2
time consuming.
сread cMap c Buffer сmerge , | red | 0 ,
сm N , N F
cread cMap cWrite , otherwise
SFP N S1
cr cshuffle cmergeR creduce cWrite . 1 F N SIP, N F
2
CMap and creduce are the cost of user-define functions, so
for each join algorithm it is calculated by the own
formula. Another cost values from (cread, cBuffer, cWrite, cmerge p(buf ) * wid * rwl * (2 * SIP N N * cC )
cmerge, cmergeR, cshuffle, ct) are common for join algorithms.
Consider these costs in more detail as [15, 24]. Stages
p(buf ) * mC * ( SIP N )
of reading input data from HDFS and writing into After that stage map output transferred to the reducers
HDFS are calculated by: (this cost includes the cost for all reducers).
сread s(split ) * rh , cWrite s(out ) * wh , | nodes 1 |
ctr s(outm)* | map | * * tC
where split is input split for mapper task, out is the | nodes |
output data of job. The buffering phase is more
complicated; during this stage three processes take The data from mappers are transferred by segments to
place: partitioning, sorting and spilling to disk. reducers. Without considering the data skew, it is
cBuffer s( split ) * rwl p(outm ) * ( pC sC assumed that the sizes of segments are the same.
s(outm)
p(buf ) s( seg )
cC sortC * log 2 | red |
| red | When segment arrive to the reducer it is placed in
Where outm is output from map functions, buf is buffer shuffle buffer or if size of segment is greater than 25%
for this stage. The buffer is divided into two parts, there of buffer size then it is spilled into disk without in-
are serialization buffer (SB), that contains key-value memory buffer. The buffer size is determined by the
pairs and an accounting buffer (AB) that contains the configuration parameters as:
metadata. So, the number of pairs in buffer is: s(buf ) shuBP * memT. If buffer reaches size
p(buf ) min{ p(SB), p( AB)} threshold (s(thr)) or the number of segments is greater
than memMT, then segments are merged, sort and spill
sortMB * 2 20 * (1 sortRP) * sortSP
p( SB) into disk. s(thr ) s(buf ) * shuMP . The number of
wid segments (|segF|) in shuffle file and the number of such
files (|shF|) are:
sortMB * 2 20 * sortRP * sortSP 1, s ( seg ) 0,25 s (buf )
p( AB )
16 s (thr )
The number of spilled files (N) from this stage is:
| segF | , s ( seg ) 0,25 * s (buf )
s ( seg )
p(out ) memMT , | segF | memMT
N
p(buf )
Then all spilled files must be merged with such | map |
| shF |
features: | segF |
the number of spill files are merged at once is F, If the number of shuffle files is greater than (2*F-1)
assume that the following N F ,
2
then all files are merged into one. So, all segments may
at first pass it is merged so spill files that remain be divided on three states: in-memory buffer (segMB),
files is multiplies F shuffle unmerged files (segUF) and shuffle merged files
at final merge if needed the combiner will be (segMF).
used.
| segMB || map | mod | segF | In map function source tag is assign to each pair
(consider that input map pair is equal to output map
0, | shF | 2 * F 1 pair):
| segMF | | shF | 2 * F 1 GRSJ
сMap p(outm) * ct , wid wid 0,000000953
F 1
In reduce function pairs with different tags are joined
| segUF || shF | F * | segMF | (nested-loop):
The cost of shuffle stage is:
p(inpr ) 2
сsfuffle | segF | *s ( seg ) * selC * rwl * (| shF | GRSJ
сreduce * selР p(inpr ) * ct
2
| segMF | *2) | segMF | * | segF | * p( seg ) *
As opposite to General reducer-side join, the cost of
* mC | map | * p( seg ) * (mC cC ) * I Optimized reducer-side join includes the cost of
combine function and the cost of reduce function is less
1, s ( seg ) 0,25 * s (buf )
I GRSJ
then сreduce :
0
Thereafter, segMB,segUF, segMF files must be merged. p(inpr ) p(inpr ) 2
Some segments from memory (segE) are spilled to disk
ORSJ
сreduce * selР * ct ,
2 2
by redBP constraint.
| segMB | *s( seg ) redBP * memT сMap сMap , wid wid 0,00000190734
ORSJ GRSJ
| segE | s( seg )
In contrast to the previous join, MR program of the
0, | segMB | *s( seg ) redBP * memT Hybrid Hadoop join consist of pre-processing job and
join job. The pre-processing job is partition one dataset
into |red| parts, and besides these partitions may be got
If the number of files from disk is less than F then segE from other MR job or from default MR job. The costs
files are merged separately. of default map and reduce functions are:
| segE | *s( seg ) prep
сMap creduce
prep
p(in1) * ct
s(m1)
0, | segUF | | segMF | F There are two ways to deliver full one dataset to the
mapper: read file from HDFS or by using distributed
After the merging, the number of files from disk is:
cache. And if distributed cache is used then the
| segUF | | segMF | 1, s(m1) 0 necessary files are copied to the slave nodes before the
| segD | job is started. So, the ctr cost is added. The costs of with
| segUF | | segMF | | segE | and without distributed cache deliver are:
Then the process of merging is similar to c merge, where
N=|segD|. ccache s(in1) * wrl p(in1) * ct
SIP c s(in1) * rh p(in1) * ct
s(m2) * ( s( segUF ) s( segMF ) s( segE )) hdfs
N The map and reduce functions costs of join job are:
At final it is merged remained files. hyb
сMap сMap
prep
(in 2),
| segR | SFP * (| segMB | | segE |)
ccache p(in1) * p(in 2) * selР * ct
N | segR | hyb
creduce
chdfs p(in1) * p(in 2) * selР * ct
SIP
s (m3) * | map | *s( seg ) 4.4 Cost model for Map-Side join
N
The final cost of this phase is: The join job doesn’t have reducer phase.
mC Map-side partition join consists of pre-processing jobs
сmergeR ( s(m1) s(m2) s(m3)) * rwl for two input datasets (or partitions are got from another
wid job) and join job.
The map function of join job is:
Since the join algorithms are known in advance we can MSPJ
creduce сreduce
hyb
more accurately than the approach in [28] is to estimate
the cost of user-defined functions Map and Reduce. In-Memory Join the small dataset (in1) is broadcast to
all reducers.
4.3 Cost model for Reduce-Side join p(in1)
ccache p(in 2) * | red | * selР * ct
In case of General reducer-side join, MR program IMMJ
сMap
consists of one job and cost for combining is equal to 0.
c p(in 2) * p(in1) * selР * c
hdfs | red |
t
In reversed join the datasets are reversed, in2 (the value, where value is the remaining attributes.
bigger one) is broadcast, in1 is split of smaller dataset Generation of synthetic data was done as in [4]. Join
and it is loaded in hash table. keys are distributed randomly.
p (in1) * ct p (in 2) * wrl p (in1) *
p (in 2)
* 5.2 Cluster configuration
* selР * ct , cache
| red | Cluster consists of three virtual machines, where one of
сMap
rev
them is master and slave at the same time, the
p (in1) * ct p (in 2) * rh p (in1) * remaining two are the slaves. Host configuration
p (in 2) consists of 1 processor, 512 mb of memory for the
* * selР * ct master, for others nodes have by 512 mb, 5 gb is the
| red | disk size. Hadoop 20.203.0 runs on Ubuntu 10.10.
Multi-phase map join cost equal to sum of immj job
s(in1) 5.3 The General Case
costs. The number of summands is 1.
memT The base idea of this experiment is to compare
4.5 The semi-join cost executions time of different phases of various
algorithms. Some parameters are fixed: the number of
The semi-join with selection consists of two jobs: Map and Reduce tasks is 3, the input size is
finding unique keys and filter the dataset by unique 10000*100000 and 1000000*1000000 tuples.
keys. The cost of map function of finding unique keys is
sum of filling hash table and producing the output costs.
The input for this job is one dataset.
find
сMap p(in1) * 2 * ct . The reduce function of that
job is run on the one reducer and the same as default
reduce function.
The filtering job consists of one map phase, where the
file with unique key from previous job is loaded into
hash table and then the split of another dataset is probe.
ccache p(in ) * ct
fil
сMap
chdfs p(in ) * ct
Figure 1: Executions time of different phases of various
algorithms. Size 10000*100000.
The Adaptive semi-join is similar to reduce-side join.
The two datasets are read and tagged by label in map
function. And at reducer the pairs with different tags are
output. The cost is equal to default job. But at the actual
join it is needed to add some cost of loading file with
unique keys, filling hash table and filtering useless pairs
fil
as сMap .
In case of semi-join with bloom-filter the program
consists of two jobs: creating bloom filter and filtering
the dataset. In the map function, bloom filter for split
constructed and the output all filter as one pair.
Figure 2: Executions time of different phases of various
bloom
сMap p(in1) * ct s(bloom) * lo algorithms. Size 1000000*1000000.
Where lo is the cost for processing bloom filter. The
reducer is one and it is combine all bloom-filter into For a small amount of data, Map phase, in which all
one. tuples are tagged, and Shuffle phase, in which data are
transferred from one phase to another, are more costly
duce | map | *s(bloom) * lo
bloom
сRe
in Reduce-Side joins. It should be noted that GRSJ is
At another job the constructed bloom-filter is loaded better than ORSJ on small data, but it is the same on big
and the second dataset is probed. data. It is because in first case time does not spend on
filb
сMap s(bloom) * rh p(in 2) * ct combining tuples. Possible, on the larger data ORSJ
outperform GRSJ when the usefulness of grouping by
5 Experiments key will be more significant. Also for algorithms with
pre-processing more time are spent on partitioning data.
The algorithms in memory (IMMJ and REV) are similar
5.1 Dataset
in small data. Two algorithms are not shown in the
Data are the set of tuples, which attributes are separated graph because of their bad times: JDBM-based map join
by a comma. Tuple is split into a pair of a key and a and Multi-phase map join. In large data IMMJ
algorithm could not be executed because of memory used: size of two dataset is 2000000, one of the data set
overflow. has skew 500000 of 5, and another has 10 or 1 of 5. In
case with IMMJ was memory overflow.
5.4 Semi-Join
The main idea of this experiment is to compare different
semi-join algorithms. These parameters are fixed: the
number of Map and Reduce tasks is 3, the bitmap size
of Bloom-filter is 2500000, the number of hash-
functions in Bloom-filter is 173, built-in Jenkins hash
algorithm is used in Bloom-filter. Adaptive semi-join
(ASGRSJ) does not finish because of memory
overflow. The abbreviation of Bloom-filter semi-join
for GRSJ is BGRSJ. The abbreviation of semi-join with
selection for GRSJ is SGRSJ respectively. Figure 5: Processing the data skew.
Although these experiments do not completely cover
the tuneable set of Hadoop parameters, they are shown
the advantages and disadvantages of the proposed
algorithms. The main problems of these algorithms are
time spent on pre-processing, transferring data, the data
skew, and memory overflow.
Each of the optimization techniques introduces
additional cost to the implementation of the join, so the
algorithm based on the tuneable settings and specific
Figure 3: Comparison of different semi-join implementations. data should be carefully chosen. Also important are the
5.5 Distributed cache parameters of the network bandwidth when distributed
cache are used or not used and a hardware specification
In [21] was showed that using of distributed cache is of nodes because of it is importance when speculative
not always good strategy. They suggested that the executions are on. Speculative execution reduces
problem can be a high speed network. This experiment negative effects of non-uniform performance of
was carried out for Reversed Map-Side join, because for physical nodes.
which a distributed cache can be important. Replication Based on the collected statistics such as data size,
was varied as 1, 2, 3 and size of data is fixed – how many keys will be taking part in the join, these
1000000*1000000 tuples. When data is small, the statistics may be collected as well as the construction of
difference is not always visible. In large data algorithms a range partitioner, the query planner can choose an
with distributed cache outperform approach of reading efficient variant of the join. For example, in [5] was
from a globally distributed system. proposed what-if analyses and cost-based optimization.
6 Future work
The algorithms discussed in this paper, only two sets
are joined. It is interesting to extend from binary
operation to multi argument joins. Among the proposed
algorithms, there is no effective universal solution.
Therefore, it is necessary to evaluate the proposed cost
models for join algorithms. And for this problem it is
need to use real cluster with more than three nodes in it
Figure 4: Performance of Reversed Map-Side join with and and more powerful to process bigger data, due to the
without using distributed cache. fact that the execution time on the virtual machine may
be different from the real cluster in reading/writing,
5.6 Skew data transferring data over the network and so on.
Also the idea of processing the data skew in
It is known that many of the presented algorithms are MapReduce applications from [19] can be applied to the
sensitive to the data skew. In this experiment take part join algorithms. Another direction to future work is to
such algorithms as Reduce-side join with Simple extend algorithm to support a theta-join and outer join.
Range-based Partitioner for GRSJ (GRSJRange) and An interesting area for future work is to develop,
Virtual Processor Partitionerfor GRSJ (GRSJVirtual), implement and evaluate algorithms or extended
and also for comparing in memory join: IMMJ, REV algebraic operations suitable for complex similarity
because of resistant to the skew. Fixed parameters are
queries in an open distributed heterogeneous [8] Surajit Chaudhuri, Raghu Ramakrishnan, and Gerhard
environment. The reasons to evaluate complex Weikum. Integrating db and ir technologies: What is the
structured queries are: a need to combine search criteria sound of one hand clapping? In CIDR, pages 1–12, 2005.
for different types of information; a query refinement [9] Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M.
e.g. based on user profile or feedback; advanced users Hellerstein, and Caleb Welton. Mad skills: new analysis
may need query structuring. The execution model and practices for big data. Proc. VLDB Endow., 2:1481–
algebraic operation to be implemented are outlined in 1492, August 2009.
[31]. The main goal is to solve the problems presented [10] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: a
in [8] as a problem. flexible data processing tool. Commun. ACM, 53:72–77,
In addition, one of the issues is efficient physical January 2010.
representation of data. Binary formats are known to [11] Jeffrey Dean, Sanjay Ghemawat, and Google Inc.
outperform the text both in speed reading and Mapreduce: simplified data processing on large clusters.
partitioning key / value pairs, and the transmission of In In OSDI04: Proceedings of the 6th conference on
compressed data over the network. Along with the Symposium on Opearting Systems Design &
Implementation. USENIX Association, 2004.
binary data format, column storage has already been
proposed for paradigm MapReduce. It is interesting to [12] Leonidas Fegaras, Chengkai Li, and Upa Gupta. An
find the best representation for specific data. optimization framework for map-reduce queries. In
EDBT 2012, march 2012.
7 Conclusion [13] Avrilia Floratou, Jignesh M. Patel, Eugene J. Shekita,
and Sandeep Tata. Column-oriented storage techniques
for mapreduce. Proc. VLDB Endow., 4:419–429, April
In this work we describe the state of the art in the area 2011.
of massive parallel processing, presented our [14] Alan F Gates. Programming Pig. O’Reilly Media, 2011.
comparative study of these algorithms, cost models and
our outline directions of future work. [15] Herodotos Herodotou. Hadoop performance models.
CoRR, abs/1106.0940, 2011.
[16] Herodotos Herodotou and Shivnath Babu. Profiling,
what-if analysis, and cost-based optimization of
References mapreduce programs. PVLDB, 4(11):1111– 1122, 2011.
[1] Daniel J. Abadi, Samuel R. Madden, and Nabil Hachem. [17] Eaman Jahani, Michael J. Cafarella, and Christopher R´e.
Column-stores vs. row-stores: how different are they Automatic optimization for mapreduce programs. Proc.
really? In Proceedings of the 2008 ACM SIGMOD VLDB Endow., 4:385–396, mar 2011.
international conference on Management of data,
SIGMOD ’08, pages 967–980, New York, NY, USA, [18] Dawei Jiang, Anthony K. H. Tung, and Gang Chen.
2008. ACM. Map-join-reduce: Toward scalable and efficient data
analysis on large clusters. IEEE Transactions on
[2] Azza Abouzeid, Kamil Bajda-Pawlikowski, Daniel Knowledge and Data Engineering, 23:1299– 1311, 2011.
Abadi, Avi Silberschatz, and Alexander Rasin.
Hadoopdb: an architectural hybrid of mapreduce and [19] YongChul Kwon, Magdalena Balazinska, Bill Howe, and
dbms technologies for analytical workloads. Proc. VLDB Jerome Rolia. A study of skew in mapreduce
Endow., 2:922–933, August 2009. applications. Moskow, Russia, june 2011. In the 5th
Open Cirrus Summit.
[3] Foto N. Afrati and Jeffrey D. Ullman. Optimizing joins
in a map-reduce environment. In Proceedings of the 13th [20] Yuting Lin, Divyakant Agrawal, Chun Chen, Beng Chin
Ooi, and Sai Wu. Llama: leveraging columnar storage for
International Conference on Extending Database
Technology, EDBT ’10, pages 99–110, New York, NY, scalable join processing in the mapreduce framework. In
USA, 2010. ACM. Proceedings of the 2011 international conference on
Management of data, SIGMOD ’11, pages 961–972,
[4] Fariha Atta. Implementation and analysis of join New York, NY, USA, 2011. ACM.
algorithms to handle skew for the hadoop mapreduce
framework. Master’s thesis, MSc Informatics, School of [21] Gang Luo and Liang Dong. Adaptive join plan
Informatics, University of Edinburgh, 2010. generation in hadoop. Technical report, Duke University,
2010.
[5] Shivnath Babu. Towards automatic optimization of
mapreduce programs. In Proceedings of the 1st ACM [22] Christine Morin and Gilles Muller, editors. European
symposium on Cloud computing, SoCC ’10, pages 137– Conference on Computer Systems, Proceedings of the
142, New York, NY, USA, 2010. ACM. 5th European conference on Computer systems, EuroSys
2010, Paris, France, April 13-16, 2010. ACM, 2010.
[6] Spyros Blanas, Jignesh M. Patel, Vuk Ercegovac, Jun
Rao, Eugene J. Shekita, and Yuanyuan Tian. A [23] Alper Okcan and Mirek Riedewald. Processing theta-
comparison of join algorithms for log processing in joins using mapreduce. In Proceedings of the 2011
mapreduce. In Proceedings of the 2010 international international conference on Management of data,
conference on Management of data, SIGMOD ’10, pages SIGMOD ’11, pages 949–960, New York, NY, USA,
975–986, New York, NY, USA, 2010. ACM. 2011. ACM.
[7] A Chatzistergiou. Designing a parallel query engine over [24] Konstantina Palla. A comparative analysis of join
map/reduce. Master’s thesis, MSc Informatics, School of algorithms using the hadoop map/reduce framework.
Informatics, University of Edinburgh, 2010. Master’s thesis, MSc Informatics, School of Informatics,
University of Edinburgh, 2009.
[25] Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J.
Abadi, David J. DeWitt, Samuel Madden, and Michael
Stonebraker. A comparison of approaches to large-scale
data analysis. In Proceedings of the 35th SIGMOD
international conference on Management of data,
SIGMOD ’09, pages 165–178, New York, NY, USA,
2009. ACM.
[26] Donovan A. Schneider and David J. DeWitt. A
performance evaluation of four parallel join algorithms in
a shared-nothing multiprocessor environment. SIGMOD
Rec., 18:110–121, June 1989.
[27] Rares Vernica, Michael J. Carey, and Chen Li. Efficient
parallel set-similarity joins using mapreduce. In
Proceedings of the 2010 international conference on
Management of data, SIGMOD ’10, pages 495–506,
New York, NY, USA, 2010. ACM.
[28] Vertica Systems, Inc. Managing Big Data with Hadoop
& Vertica, 2009.
[29] Guanying Wang, Ali Raza Butt, Prashant Pandey, and
Karan Gupta. A simulation approach to evaluating design
decisions in mapreduce setups. In MASCOTS, pages 1–
11. IEEE, 2009.
[30] Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, and D.
Stott Parker. Map-reduce-merge: simplified relational
data processing on large clusters. In Proceedings of the
2007 ACM SIGMOD international conference on
Management of data, SIGMOD ’07, pages 1029–1040,
New York, NY, USA, 2007. ACM.
[31] Anna Yarygina, Boris Novikov, and Natalia Vassilieva.
Processing complex similarity queries: A systematic
approach. In Maria Bielikova, Johann Eder, and A Min
Tjoa, editors, ABDIS 2011 Research Communications:
Proceedings II of the 5th East-European Conference on
Advances in Databases and Information Systems 20 – 23
September 2011, Vienna, pages 212–221. Austrian
Computer Society, September 2011.
[32] Minqi Zhou, Rong Zhang, Dadan Zeng, Weining Qian,
and Aoying Zhou. Join optimization in the mapreduce
environment for column-wise data store. In Proceedings
of the 2010 Sixth International Conference on Semantics,
Knowledge and Grids, SKG ’10, pages 97–104,
Washington, DC.