Mostafa Bamha
email: mostafa.bamha@univ-orleans.fr

Frédéric Loulergue
email: frederic.loulergue@univ-orleans.fr

A Scalable and Skew-insensitive Algorithm for Join Operations using Map/Reduce Model

Keywords: Join operations, Data skew, Map/Reduce model, Hadoop framework

For over a decade, Map/Reduce has become a prominent programming model to handle vast amounts of raw data in large scale systems. This model ensures scalability, reliability and availability aspects with reasonable query processing time. However these large scale systems still face some challenges : data skew, task imbalance, high disk i/o and redistribution costs can have disastrous effects on performance.

In this paper, we introduce MRFA-Join algorithm: a new Frequency Adaptive algorithm based on Map/Reduce Programming model and distributed histograms for join processing on large-scale datasets. A cost analysis of this algorithm shows that our approach is insensitive to data skew and ensures perfect balancing properties during all stages of join computation. Performances have been experimented on Grid'5000 infrastructure.

Introduction

Join operation is one of the most widely used operations in relational database systems, but it is also a heavily time consuming operation. For this reason it was a prime target for parallelization. The join of two relations R and S on attribute A of R and attribute B of S (A and B of the same domain) is the relation, written R ✶ S, obtained by concatenating the pairs of tuples from R and S for which R.A = S.B.

Parallel join usually proceeds in two phases: a redistribution phase (generally based on join attribute hashing) and then a sequential join of local fragments. Many parallel join algorithms have been proposed. The principal ones are: Sort-merge join, Simple-hash join, Grace-hash join and Hybrid-hash join [START_REF] Schneider | A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment[END_REF]. All of them (called hashing algorithms) are based on hashing functions which redistribute relations such that all the tuples having the same attribute value are forwarded to the same node. Local joins are then computed and their union is the output relation. Research has shown that join is parallelizable with near-linear speed-up on distributed architectures but only under ideal balancing conditions : data skew may have disastrous effects on the performance [START_REF] Mourad | Limits of parallelism in hash join algorithms[END_REF][START_REF] Seetha | Effectiveness of parallel joins[END_REF]. To this end, several parallel algorithms were presented to handle data skew while treating join queries on parallel database systems [1-3, 8, 13, 16].

Today with the rapid development of network technologies, Internet search engines and Data Mining applications, the need to manage and query a huge amount of datasets every day becomes essential. Parallel processing of such queries on hundreds or thousands of nodes is obligatory to obtain a reasonable processing time [START_REF] Dean | Mapreduce: Simplified data processing on large clusters[END_REF]. However, building parallel programs on parallel and distributed systems is complicated because programmers must treat several issues such as load balancing, fault tolerance, etc.

Search engine companies have developed Distributed File Systems (DFS) and parallel programming infrastructures that treat these parallel processing related issues without the explicit participation of the programmers [START_REF] Lämmel | Google's mapreduce programming model -revisited[END_REF]. Hadoop [10], Google's MapReduce model [START_REF] Lämmel | Google's mapreduce programming model -revisited[END_REF], Google file system [START_REF] Ghemawat | The google file system[END_REF], BigTable [START_REF] Chang | Bigtable: a distributed storage system for structured data[END_REF] are examples of such systems. These systems are built from thousands of commodity machines and assure scalability, reliability and availability aspects [START_REF] Lee | Parallel data processing with mapreduce: A survey[END_REF]. To reduce disk i/o, each file in such storage systems is divided into chunks or blocks of data and each block is replicated on several nodes for fault tolerance. Parallel programs are easily written on such systems following the Map/Reduce paradigm where a program is composed of a workflow of user defined map and reduce functions [START_REF] Dean | Mapreduce: Simplified data processing on large clusters[END_REF][START_REF] Lee | Parallel data processing with mapreduce: A survey[END_REF]. The map function operates on a (key, value) couple and produces intermediate key with an associated list of values. The reduce function merges all the intermediate couples having the same key.

In this paper we are interested in the evaluation of join operations on large scale systems using Map/Reduce model. This programming model is designed to simplify the development of large-scale, distributed, fault-tolerant data processing applications.

In [START_REF] Yang | Map-reduce-merge: simplified relational data processing on large clusters[END_REF], three well known algorithms for join evaluation were implemented using an extended Map/Reduce model. These algorithms are Sort-Merge-Join, Hash-Join and Block Nested-Loop Join. Combining this model with DFS facilitates the task of programmers because they don't need to take care of fault tolerance and load balancing issues. However, load balancing in the case of join operations is not straightforward in the presence of data-skew. In [START_REF] Blanas | A comparison of join algorithms for log processing in mapreduce[END_REF] Blanas & all. have presented an improved versions of MapReduce sort-merge joins and semi-join algorithms for Log processing, to fix the problem of buffering all records from both inner and outer relations in "Standard repartition join" provided in Hadoop Contributed join package (org.apach.hadoop.contrib). For the same reasons as in PDBMS 1 , even in the presence of integrated functionality for load balancing and fault tolerance in MapReduce, these algorithms still suffer from the effect of data skew since all tuples having the same join values in Map phase are sent to the same reducer which limits the scalability of the presented algorithms [START_REF] Lee | Parallel data processing with mapreduce: A survey[END_REF].

The aim of join operations is to combine information from two or more data sources, Unfortunately, MapReduce framework is somewhat inefficient to perform such operations since data from one source must be maintained in memory for comparison to other source of data. Consequently, adapting well-known join algorithms to MapReduce is not as straightforward as one might hope, and MapReduce programmers often use simple but inefficient algorithms to perform join operations especially in the presence of skewed data [START_REF] Blanas | A comparison of join algorithms for log processing in mapreduce[END_REF][START_REF] Lee | Parallel data processing with mapreduce: A survey[END_REF][START_REF] Pavlo | A comparison of approaches to large-scale data analysis[END_REF].

To avoid the effect of data skew in join operations using Map/Reduce model, we introduce MRFA-Join (Map/Reduce Frequency Adaptive Join) algorithm based on distributed histograms and randomized key's redistribution approach. This algorithm, inspired from our previous research on Join and Semi-join operations in PDBMS, is well adapted to manage huge amount of data on large scale systems even for highly skewed data.

The Map-Reduce Programming Model

Google's Map/Reduce programming model presented in [START_REF] Dean | Mapreduce: Simplified data processing on large clusters[END_REF] is based on two functions: Map and Reduce. Dean and Ghemawat stated that they have inspired their Map/Reduce model from Lisp and other functional languages [START_REF] Dean | Mapreduce: Simplified data processing on large clusters[END_REF]. The programmer is only required to implement two functions Map and Reduce having the following signatures: In this paper, we used an open source version of Map/Reduce called Hadoop developed by "The Apache Software Foundation". Hadoop framework includes a distributed file system called HDFS2 designed to store very large files with streaming data access patterns.

map: (k 1 , v 1 ) -→ list(k 2 , v 2 ), reduce: (k 2 , list(v 2 )) -→ list(v 3
For efficiency reasons, in Hadoop Map/Reduce framework, users may also specify a "Combine function", to reduce the amount of data transmitted from Mappers to Reducers during shuffle phase (see fig 1). The "Combine function" is like a local reduce applied (at map worker) before storing or sending intermediate results to the reducers. The signature of Combine function is:

combine: (k 2 , list(v 2 )) -→ (k 2 , list(v 3 )).
To cover a large range of applications need in term of computation and data redistribution, in Hadoop framework, the user can optionally implement two additional functions : init() and close() called before and after each map or reduce task. The user can also specify a "partition function" to send each key k 2 generated in map phase to a specific reducer destination. The reducer destination may be computed using only a part of the input key k 2 (Hadoop's default "partition function" is based on "hashing" the whole input key k 2 ). The signature of the partition function is : partition:

(Key k 2 ) -→ Integer. /* Integer is between 0 and the number of reducers #numReduceTasks */

MRFA-Join: A Map-Reduce Skew Insensitive Join Algorithm

As stated in the introduction section, Map/Reduce hash based join algorithms presented in [START_REF] Blanas | A comparison of join algorithms for log processing in mapreduce[END_REF][START_REF] Yang | Map-reduce-merge: simplified relational data processing on large clusters[END_REF] may be inefficient in the presence of highly skewed data [START_REF] Pavlo | A comparison of approaches to large-scale data analysis[END_REF] due to the fact that in Map function in these algorithms, all the key-value pairs (k 1 , v 1 ) representing the same entry for the join attribute are sent to the same reducer (In Map phase, emitted key-value pairs (k 2 , v 2 ), the key k 2 is generated by only using join attribute values in the manner that all records with the same join attribute value will be forwarded to the same reducer).

To avoid the effect of repeated keys, Map user-defined function should generate distinct output keys k 2 even for records having the same join attribute value. This is made possible by using a user defined partitioning function in Hadoop : the reducer destination for a key k 2 can be computed from different parts of key k 2 and not by a simple hashing of all input key k 2 . To this end, we introduce, in this section, a join algorithm called MRFA-Join (Map/Reduce Frequency Adaptive Join) based on distributed histograms and a random redistribution of repeated join attribute values combined with an efficient technique of redistribution where only relevant data is redistributed across the network during the shuffle phase of reduce step. A cost analysis for MRFA-Join is also presented to give for each computation step, an upper bound of execution time in order to prove the strength of our approach.

In this section, we describe the implementation of MRFA-Join using Hadoop MapReduce framework as it is, without any modification. Therefore, the support for fault tolerance and load balancing in MapReduce and Distributed File System are preserved if possible: the inherent load imbalance due to repeated values must be handled efficiently by the join algorithm and not by the Map/Reduce framework.

To compute the join, R ✶ S, of two relations (or datasets) R and S, we assume that input relations R and S are divided into blocks (splits) of data. These splits are stored in Hadoop Distributed File System (HDFS). These splits are also replicated on several nodes for reliability issues. Throughout this paper, for a relation T ∈ {R, S}, we use the following notations: • c r/w : read/write cost of a page of data from/to distributed file system (DFS),

•
• c comm : communication cost per page of data, • t i s : time of a simple search in a Hashtable on node i, • t i h : time to add an entry to a Hashtable on node i, • N B mappers: number of job mapper nodes,

• N B reducers: number of job reducer nodes.

We will describe MRFA-Join algorithm while giving a cost analysis for each computation phase. Join computation in MRFA-Join proceeds in two map-reduce jobs: a. the first map-reduce job is performed to compute distributed histograms and to create randomized communications templates to redistribute only relevant data while avoiding the effect of data skew, b. the second one, is used to generate join output result by using communications templates carried out in the previous step.

In the following, we will describe MRFA-Join steps while giving an upper bound on the execution time of each map-reduce step. The O(. . .) notation only hides small constant factors: they only depend program's implementation but neither on data nor on the machine parameters. Data redistribution in MRFA-Join algorithm is the basis for efficient and scalable join processing while avoiding the effect of data skew in all the stages of join computation. MRFA-Join algorithm proceeds in 4 steps: Emitted couples (<K,tag>,1) are then combined and partitioned using a user defined partitioning function by hashing only key part K and not the whole mapper tagged key <K,tag>. The result of combine phase is then sent to reducers destination in the shuffle phase of the following reduce step. The cost of this step is at most :

T ime(a.1.2) = O N B mappers max i=1 ||Hist map (R map i )|| * log ||Hist map (R map i )|| + ||Hist map (S map i )|| * log ||Hist map (S map i )||) + ccomm * (|Hist map (R map i )| + |Hist map (S map i )| .
And the global cost of this step is: T ime stepa.1 = T ime(a.1.1) + T ime(a.1.2).

We recall that, in this step, only local histograms Hist map (R map Hist map (S map j

)) of each mapper j. Received Hist red i (R) and Hist red i (S) are then merged to compute global histogram HistIndexi(R ✶ S) on each reducer i. HistIndex(R ✶ S) is used to compute randomized communication templates for only records associated to relevant join attribute values (i.e. values which will effectively be present the join result). In this step, each reducer i, computes the global frequencies for join attribute values which are present in both left and right relations and emits, for each join attribute K, an entry of the form : (K,<Frequency index(K),Nb buckets1(K),Nb buckets2(K)>) where:

-F requency index(K) ∈ {0, 1, 2} will allow us to decide if, for a given relevant join attribute value K, the frequencies of tuples of relations R and S having the value K are greater (resp. smaller) than a defined threshold frequency f 0 . It also permits us to choose dynamically the probe and the build relation for each value K of the join attribute. This choice reduces the global redistribution cost to a minimum. For a given join attribute value K ∈ HistIndexi(R ✶ S), ➠ F requency index(K) = 0, means that the frequency of tuples of relations R and S, associated to value K, are less than the threshold frequency:

Hist red i (R)(K) < f0
and Hist red i (S)(K) < f0, ➠ F requency index(K) = 1, means that the frequency of tuples of relation R having the value K are greater than the threshold frequency, and the frequency in relation R for this attribute value is greater than the corresponding frequency in relation S:

Hist red i (R)(K) ≥ f0 and Hist red i (R)(K) ≥ Hist red i (S)(K).
➠ F requency index(K) = 2, means that the frequency of tuples of relation S associated to value K are greater than the threshold frequency, and the frequency in relation S for this attribute value is greater than the corresponding frequency in relation R: Hist red i (S)(K) ≥ f0 and Hist red i (S)(K) > Hist red i (R)(K),

-Nb buckets1(K): is the number of buckets used to partition records of relation associated to the highest frequency for join attribute value K, -Nb buckets2(K): is the number of buckets used to partition records of relation associated to the lowest frequency for join attribute value K.

For a join attribute value K, the number of buckets N b buckets1(K) and N b buckets2(K) are generated in a manner that each bucket will fit in reducer's memory. This makes the algorithm insensitive to the effect of data skew even for highly skewed input relations. Using this information, each reducer i, has local knowledge of how relevant records of input relations will be redistributed in the next following map phase.

t i h * ||HistIndex(R ✶ S)||).
Once the local hash table is created on each mapper, input relations are then read from DFS, and each record is either discarded (if record's join key is not present in the local hash table) or routed to a designated random reducer destination using communication templates computed in step a.2 (Map phase details are described in Algorithm 6). Figure 2 gives an example of communication templates used to partition data for HistIndex (K,Tag1) (K,Tag2)

(K,i 0 ,1,Tag1) (K,i 1 ,1,Tag1) (K,i 2 ,1,Tag1) (K,i 3 ,1,Tag1) (K,i 1 ,2,Tag2,0) (K,i 2 ,2,Tag2,0) (K,i 4 ,2,Tag2,0) (K,i 3 ,2,Tag2,0) (K,i 0 ,2,Tag2,0) (K,i 1 ,2,Tag2,1) (K,i 2 ,2,Tag2,1) (K,i 4 ,2,Tag2,1) (K,i 3 ,2,Tag2,1) (K,i 0 ,2,Tag2,1) (K,i 1 ,2,Tag2,2) (K,i 2 ,2,Tag2,2) (K,i 4 ,2,Tag2,1) (K,i 3 ,2,Tag2,2) (K,i 0 ,2,Tag2,2) (K,i 4 ,1,Tag1) Reducer: i 0 Reducer: i 1 = i 0 +1 Reducer: i 2 = i 0 +2 Reducer: i 3 = i 0 +3 Reducer: i 4 = i 0 +4
Figure 2: Generated buckets associated to a join key K corresponding to a high frequency where records from relation associated to T ag 1 (i.e relation having the highest frequency) are partitioned into five buckets and those of relation associated to T ag 2 are partitioned into three buckets.

entry (K,<Frequency index(K),Nb buckets1(K),Nb buckets2(K)>), corresponding to a join attribute K associated to a high frequency, into small buckets. In this example, data associated to relation corresponding to T ag 1 is partitioned into 5 buckets (i.e. N b buckets1(K) = 5) where as those of relation corresponding to T ag 2 is partitioned into 3 buckets (i.e. N b buckets2(K) = 3). For these buckets, appropriate map keys are generated so that all records in each bucket of relation associated to T ag 1 are forwarded to the same reducer holding all the buckets of relation associated to T ag 2 . This partitioning guarantees that join tasks, are generated in a manner that the input data for each join task will fit in the memory of processing node and never exceed a user defined size, even for highly skewed data.

The global cost of this step is at most:

T imestep a.2 = O N B reducers max i=1 (||Hist red i (R)|| + ||Hist red i (S)||) .
Note that, HistIndex(R ✶ S) ≡ ∪i(Hist red i (R) ∩ Hist red i (S)) and ||HistIndex(R ✶ S)|| is very small compared to ||Hist red (R)|| and ||Hist red (S)||. The cost of this step is :

T ime(b.1.2) = O N B mappers max i=1 (c r/w * (|R map i | + |S map i |) + t i s * (||R map i || + ||S map i ||)+ ||R map i || * log ||R map i || + ||S map i || * log ||S map i || + ccomm * (|R map i | + |S map i |)) , the term c r/w * (|R map i | + |S map i |) is
map i | + |S map i |)
) is time to communicate relevant data from mappers to reducers, using our communication templates described in step a.2.

Hence the global cost of this step is:

T ime step b.1 = T ime(b.1.1) + T ime(b.1.2).
We recall that, in this step, only relevant data is emitted by mappers (which reduces communication cost in the shuffle step to a minimum) and records associated to high frequencies (those having a large effect on data skew) are redistributed according to an efficient dynamic partition/replicate schema to balance load among reducers and avoid the effect of data skew. However records associated to low frequencies (these records have no effect on data skew) are redistributed using hashing functions. b.2 Reduce phase to compute join result:

At the end of step b. ) of each mapper j and performs a local join of received data. This reduce phase is described in detail in Algorithm 8. The cost of this step is:

T imestep b.2 = O( N B reducers max i=1 (||R red i || + ||S red i || + c r/w * |R red i ✶ S red i |).
The global cost of MRFA-Join is therefore the sum of the above four steps :

T ime M RF A-Join = T ime stepa.1 + T ime stepa.2 + T ime step b.1 + T ime step b.2
Using hashing technique, the join computation of R ✶ S requires at least the following lower bound : bound inf = MRFA-Join algorithm has asymptotic optimal complexity when:

||HistIndex(R ✶ S)|| ≤ max N B mappers max i=1 (||R map i || * log ||R map i ||, |S map i || * log ||S map i ||), N B reducers max i=1 ||R red i ✶ S red i ||) , (1)
this is due to the fact that, all other terms in T ime M RF A-Join are bounded by those of bound inf . Inequality 1 holds, in general, since HistIndex(R ✶ S) contains only distinct values that appear in both relations R and S.

Remark: In practice, data imbalance related to the use of hashing functions can be due to:

• a bad choice of used hash function. This imbalance can be avoided by using the hashing techniques presented in the literature making it possible to distribute evenly the values of the join attribute with a very high probability [START_REF] Carter | Universal classes of hash functions[END_REF],

• an intrinsic data imbalance which appears when some values of the join attribute appear more frequently than others. By definition a hash function maps tuples having the same join attribute values to the same processor. There is no way for a clever hash function to avoid load imbalance that results from these repeated values [START_REF] Dewitt | Practical Skew Handling in Parallel Joins[END_REF]. But this case cannot arise here owing to the fact that histograms contain only distinct values of the join attribute and the hashing functions we use are always applied to histograms or applied to randomized keys.

Experiments

To evaluate the performance of MRFA-Join algorithm presented in this paper, we compared our algorithm to the best known solutions called respectively Improved Repartition Join and Standard Repartition Join. Improved Repartition Join was introduced by Blanas et al. in [START_REF] Blanas | A comparison of join algorithms for log processing in mapreduce[END_REF], where as Standard Repartition Join is the join algorithm provided in Hadoop framework's contributions. We ran a large series of experiments on the Grid'5000 testbed where 50 nodes were randomly selected from three clusters of Grid'5000 Sophia's site. Nodes characteristics are described in Table 1. Setting up a Hadoop cluster consisted of deploying each centralized entity (namenode and jobtracker) on a dedicated machine and co-deploying datanodes and tasktrackers on the rest of the nodes. Typically, we used a separate machine as a Hadoop client to manage job submissions. Data replication parameter was fixed to three in Hadoop Distributed File System (HDFS) configuration file. To study the effect of data skew on performance, join attribute values in the generated data have been chosen to follow a Zipf distribution [START_REF] Zipf | Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology[END_REF] as it is the case in most database tests: Zipf factor was varied 0 (for a uniform data distribution) to 1.0 (for a highly skewed data). Input relations size was fixed to 200M records for the right relation (approximately 20GB of data) and 10M of records for the left relation (approximately 1GB of data) and the join result varying from approximately 40M to 1700M records (corresponding respectively to about 8GB and 340GB of output data).

We noticed in all the tests and also those presented in figure 3, that our MRFA-Join algorithm outperforms both Improved Repartition Join and Standard Repartition Join algorithms even for low or moderated skew. We recall that our algorithm requires the scan of input data twice : the fist one for histogram processing and the second scan is performed for join processing. The cost analysis and tests performed showed that the overhead related to histogram processing is compensated by the gain in join processing since only relevant data (that appears in the join result) is emitted by mappers in the map phase which reduce considerably the amount of data transmitted over the network in shuffle phase (see figure 4). Moreover, for skew factors varying from 0.6 to 1.0, both Improved Repartition Join and Standard Repartition Join jobs fails

Conclusion and future work

In this paper, we have introduced the first skew-insensitive join algorithm, called MRFA-Join, using Map/Reduce model, based on distributed histograms and randomized keys redistribution for highly skewed data. The detailed information provided by these histograms, allows us to reduce communications costs to only relevant data while guaranteeing perfect balancing processing due to the fact that, all the generated join tasks and buffered data do not never exceed a user defined size using threshold frequencies. This makes the algorithm scalable and outperforming existing map-reduce join algorithms which fail to handle skewed data whenever a join task can not fit in the available node's memory. We mention that MRFA-Join can also benefit from Map/Reduce underlying load balancing framework in heterogeneous or a multi-user environment since MRFA-Join is implemented without any change in Map/Reduce framework.

Our experience with the join operations shows that the overhead related to distributed histograms processing remains very small compared to the gain in performance and communication costs since only relevant data is processed or redistributed across the network. Future work will consist in studying how distributed histograms could be used to compute more complex or pipelined join queries to reduce communication and disk input/output costs for intermediate data for each sub-query by generating only relevant data.

Figure 1 :

 1 Figure 1: Map-reduce framework.

  |T |: number of pages (or blocks of data) forming T , • ||T ||: number of tuples (or records) in relation T , • T : the restriction (a fragment) of relation T which contains tuples which appear in the join result. ||T || is, in general, very small compared to ||T ||, • T map i : the split(s) of relation T affected to mapper i, • T red i : the split(s) of relation T affected to reducer i, • T i : the split(s) of relation T affected to mapper i, • ||T i ||: number of tuples in split T i , • Hist map (T map i ): Mapper's local histogram of T map i , i.e. the list of pairs (v, n v ) where v is a join attribute value and n v its corresponding frequency in relation T map i on mapper i, • Hist red i (T ) : the fragment of global histogram of relation T on reducer i, • Hist red i (T )(v) is the global frequency n v of value v in relation T , • HistIndex(R ✶ S): join attribute values that appear in both R and S and their corresponding three parameters: Frequency index, Nb buckets1 and Nb buckets2 used in communications templates,

a. 1

 1 Map phase to generate a tagged "local histogram" for input relations: In this step, each mapper i reads its assigned data splits (blocks) of relation R and S from distributed file system (DFS) and emits a couple (<K,tag>,1) for each record in R map i (resp. S map i ) where K is join key value and tag represents input relation tag. The cost of this step is :T ime(a.1.1) = O

i)

  and Hist map (S map i ) are sorted and transmitted across the network and the size of these histograms are very small compared to the size of input relations R map i and S map i owing to the fact that, for a relation T , Hist map (T ) contains only distinct entries of the form (v, n v ) where v is a join attribute value and n v the corresponding frequency. a.2 Reduce phase to create join result global histogram index and randomized communication templates for relevant data: At the end of shuffle phase, each reducer i will receive a fragment of Hist red i (R) (resp. Hist red i (S)) obtained through hashing of distinct values of Hist map (R map j ) (resp.

  c r/w * (|R map i | + |S map i |) is the cost of reading input relations from DFS on node i. The term ||R map i || * log ||R map i ||+||S map i || * log ||S map i || represents the cost to sort input relations records on map phase. The term ccomm * (|R map i | + |S map i |) represents the cost to communicate data from mappers to reducers, the term ||R red i || + ||S red i || is time to scan input relations on reducer i and c r/w * |R red i ✶ S red i | represents the cost to store reducer's i join result on the DFS.

6✄Algorithm 3

 3 Appendix: Implementation of MRFA-Join functions Algorithm 1 MRFA-join algorithm workflow. a.1◮ Map phase: /* To generate a tagged "Local histogram" for input relations */ ✄ Each mapper i reads its assigned data splits (blocks) of relation R map i and S map i from the DFS ✄ Extract the join key value from input relation's record. ✄ Get a tag to identify source input relation. ✄ Emit a couple ((join key,tag),1) /* a tagged join key with a frequency 1 */ ◮ Combine phase: To compute local frequencies for each join key value in relations R map i and S map i Each combiner, for each pair (join key,tag) computes the sum of generated local frequencies associated to the join key value in each tagged join key generated in Map phase. ◮ Partition phase: ✄ For each emitted tagged join key, computes reducer destination according to only join key value. a.2◮ Reduce phase: /* To combine Shuffle's output records and to create Global join histogram index */ ✄ Compute the global frequencies for only join key values present in both relations R and S. ✄ Emit, for each join key, a couple (join key,(frequency index,Nb buckets1, Nb buckets2)). /* frequency index ∈ {0, 1, 2} used to get detailed information about data distribution in R and S */ b.1◮ Map phase: ✄ Each mapper, i, reads join result Global histogram index from DFS, and creates a local Hashtable. ✄ Each mapper, i, reads its assigned data splits (blocks) of relation R map i and S map i from DFS, and generates randomized communication templates for records in R map i and S map i according to join key value and its corresponding frequency index in HashTable. In communication templates, only relevant records from R map i and S map i are emitted using a hash or a randomized partition/replicate schema. ✄ Emit relevant randomised tagged records from relations R map i and S map i . ◮ Partition phase: ✄ For each emitted tagged join key, compute reducer destination according to the value of join key, and reducer random destination generated in Map phase; b.2◮ Reduce phase: to combine Shuffle's output records and to generate join result Algorithm 2 Map function /* To generate local histograms values and tag input relation records */ map(K: null, V : a record from a split of either relation R or S) { ✄ relation tag ← get relation tag from current relation split; ✄ join key ← extract the join column from record V of relation R; ✄ Emit ((join key,relation tag), 1); } Combine function: /* To compute local histogram's frequencies for join key */ combine(Key K,List List V ) { /* List V is the list of values "1" corresponding to the unique frequencies in relation Ri or Si emitted by Mappers */ ✄ frequency ← sum of frequencies in List V ; ✄ Emit (K,frequency); }

Distributed File System (DFS)

  ).The user must write the map function that has two input parameters, a key k 1 and an associated value v 1 . Its output is a list of intermediate key/value pairs (k 2 , v 2 ). This list is partitioned by the Map/Reduce framework depending on the values of k 2 , where all pairs having the same value of k 2 belong to the same group.The reduce function, that must also be written by the user, has two parameters as input: an intermediate key k 2 and a list of intermediate values list(v 2 ) associated with k 2 . It applies the user defined merge logic on list(v 2 ) and outputs a list of values list(v 3 ).

	Map/Reduce is a simple yet powerful framework for implementing distributed applications
	without having extensive prior knowledge of issues related to data redistribution, task allocation
	or fault tolerance in large scale distributed systems.
			bucket
		Mapper	bucket bucket
			bucket
				Reducer	split
	split split split split split	Mapper	bucket bucket bucket bucket	Reducer	split split split split split
	split		bucket		split
	split split	Mapper	bucket bucket bucket
	split			Reducer
			bucket
		Mapper	bucket bucket
			bucket

Map phase Reduce phase DFS data read. Sort, Combine and Partition data. DFS data write. Shuffle and Sort data Distributed File System (DFS)

  To guarantee a perfect balancing of the load among processing nodes, communication templates are carried out jointly by all reducers (and not by a coordinator node) for only join attribute values which are present in join result : Each reducer deals with the redistribution of the data associated to a subset of relevant join attribute values. b.1 Map phase to create a local hash table and to redistribute relevant data using randomized communication templates: In this step, each mapper i reads join result global histogram index, HistIndex, to create a local Hash table in time: T ime(b.1.1) = O(max N B mappers i=1

  || is time to sort relevant data on mapper i, where as the term ccomm * (|R

	time to read input relations from DFS on each mapper
	i, the term t i s * (||R map i input record, ||R map i || * log ||R || + ||S map i map i || + ||S ||) is the time to perform a hash table search for each map i map || * log ||S i

  1, each reducer i receives a fragment R

	through randomized hashing of R	map j	(resp. S	map j	red i	(resp. S	red i ) obtained

Table 1 :

 1 Grid'5000 -Sophia's site computing resource characteristics

	Cluster Number	CPU	CPUs	Cores	Memory	Disk
	ID	of nodes		per node per CPU	(GB)	Storage
	1	56	AMD@2.2GHz	2	2	3GB RAM	135GB
	2	50	AMD@2.6GHz	2	2	3GB RAM	232GB
	3	45	Intel@2.26GHz	2	4	31GB RAM 557GB

PDBMS: Parallel Database Management Systems.

HDFS: Hadoop Distributed File System.

Acknowledgements

Experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr). This work is also partly supported under the INEX project funded by the Conseil Général du Loiret.

due to memory lack this is due to the fact that, in reduce phase, all the records emitted by the mappers having the same join key are send and processed by the same reducer which makes both Improved Repartition Join and Standard Repartition Join algorithms very sensitive to data skew and limits their scalability. This can not occur in MRFA-Join owing to the fact that attribute values associated to high frequencies are forwarded to distinct reducers using randomized join attribute keys and not by a simple hashing of record's join key. We expect a higher gain related to histograms pre-processing in complex queries computation due to the fact that histograms can be used to reduce drastically the costs of communication and disk input/output of intermediate data by generating only relevant data for each sub-query.


Philippe Steiner

L ' Auteur

GeWoPs Working papers du GEMASS

Keywords: gift, Mauss, organisation, Polanyi, transplantation don, Mauss, organisation, Polanyi, transplantation

working papers de la Fondation Maison des sciences de l'homme.

his paper studies the speciic form of gift-giving behavior in which an organization, or a set of organizations, intervenes between the donor and the donee. It is suggested that this should be called an organizational gift, or "organic gift". Following Richard Titmuss' insights, the irst part considers what happens in the domain of biomedicine, examining here what is meant by a "gift" in the case of organ transplantation, in contrast to the Maussian conceptualization of gift-giving behavior. he second part elaborates this inding, and delineates the main characteristics of this modern form of gift, entailing the presence of organizations in the absence of which the donor and the donee could neither give nor receive. he inal part broaden the perspective, including a review of Karl Polanyi's conception of reciprocity as an integrative form of economic activity, providing a new way of mapping the forms of exchange which present themselves as alternatives to market exchange in our current market societies. Fondation Maison des sciences de l'homme -190 avenue de France -75013 Paris -France http://www.msh-paris.fr -FMSH-WP-2014-63

Sommaire

Transplant surgery and the organizational gift Organizational gift, gift and market exchange

Reciprocity and alternatives forms of exchange in modern economies

Polanyi's four types of economic integration

What "reciprocity" means in a market-system society Forms of integration and the relational dimensions of economic integration

Conclusion References

The Organizational Gift and Sociological Approaches to Exchange

5/21

Fondation Maison des sciences de l'homme -190 avenue de France -75013 Paris -France http://www.msh-paris.fr -FMSH-WP-2014-63 S ince the middle of the 19th Century sociologists have known that, beyond market exchanges, there exists in any given society a plurality of modes of transferring goods. Accordingly, they have devoted an important part of their inquiries to explaining the functioning of these alternatives to market exchanges: inheritance and gift in Auguste Comte's Système de politique positive; gift in Marcel Mauss' Essai sur le don; and symbolic exchange in a series of papers written by Pierre Bourdieu in the 1970s. I cannot here deal with the whole set of these modes of exchange, so I will focus on what happens in the domain of biomedicine, particularly in organ transplantation. he main inding is that there exists a speciic form of gift-giving behavior in which an organization, or a set of organizations, intervenes between the donor and the donee. I suggest that this should be called an organizational gift. Some of its characteristics were explained in Richard Titmuss' study, when he emphasized how the gift of blood difered from the gift considered by anthropologists; notably because there is no longer a direct connection between donor and recipient. In the organizational gift there is no similarity between both individuals, connected only by the willingness of one of them to help a sufering "stranger".

Following Richard Titmuss' insights, the irst part of the paper will consider what happens in the domain of biomedicine, examining here what is meant by a "gift" in the case of organ transplantation, in contrast to the Maussian conceptualization of gift-giving behavior. he second part of the paper will elaborate this inding, and delineate the main characteristics of this modern form of gift, entailing the presence of organizations in the absence of which the donor and the donee could neither give nor receive. In the inal part I will broaden my perspective, including a review of Karl Polanyi's conception of reciprocity as an integrative form of economic activity, providing a new way of mapping the forms of exchange which present themselves as alternatives to market exchange in current market societies.

Transplant surgery and the organizational gift

here is almost universal agreement that the surgical practice of transplantation is founded upon gift-giving behaviors; from the 1950s to the present, both medical and political worlds have relentlessly opposed the commodiication of human body parts -the only exception is in Iran, where a biomarket for kidneys was made legal in 1987. In parallel to the case of blood that was central to Titmuss's approach, organ transplantation surgery has introduced a new and unusual form of gift. However, the issue of what is actually given, and to whom, remains complex. French legislation from the 1970s up to the present-day is a case in point.

According to this legislation, organ gift-giving means essentially that the person whose organs are removed, whether dead or alive, receives nothing, whether cash or any other material advantage, as a counterpart for that person's gift. Furthermore, the French legislature decreed in 1978 that all French citizens were deemed to have given their consent to post-mortem donation. Following a lengthy debate, revision in 2011 of the bioethical law (Loi de bioéthique) made it possible, in the case of living donation, that the living donor receive some payment to cover the costs incurred in making this altruistic gift. But this payment should never be confused with any direct or indirect payment for the organ itself. Hence from the legal point of view a gift is made according to article 894 of the French Civil Code, which runs as follows: "Inter vivos donation is an act through which the donor makes an actual and irrevocable transfer to the beneit of the donee who accepts it". his form of resource transfer is clearly quite unlike the selling of a good, which is "a convention by which one party undertakes to deliver something and his counterpart undertakes to pay for it" (art. 1582); it is also unlike exchange behavior, which is deined as "a contract by which those involved give one thing in exchange for another" (art. 1702). Gift-giving behavior precludes the existence of the counterpart that is central to the deinition of both selling and exchanging. his legal deinition does not it the sociological approach of gift-giving behaviors, notably that which Mauss proposed in his famous study published in L'Année sociologique (Mauss 1925) including the three obligations: to give, to receive and to give back. here is thus a sharp contrast between the sociological and the legal approach. From the legal point of view, a gift entails free will since the idea of an obligation to part with something would be meaningless -the mingling of freedom and obligation so often mentioned in Mauss's writings is likewise ruled out. Furthermore, the idea of an obligation to receive is also absent from the legal point of view, since one article of the French Civil Code states that a donation is efective only when the donee has explicitly accepted the gift. Finally, any obligation to give back is explicitly ruled out by the deinition provided in article 894. Given such discrepancy between legal and social approaches, it is necessary to consider carefully in what sense organ donation can be considered sociologically as a gift.

In order to get a clear picture of what is meant by organ donation, I suggest that the materiality of gift-giving behavior be considered with respect to transplant surgery.

In the case of post mortem procurement, donation occurs within the medical organization. Death, and more precisely brain death, requires the intervention of several specialists able to read data provided by (usually) two successive encephalograms; if the person assigned to an intensive care unit is declared dead while her heart is still beating and her lungs inhaling oxygen and expelling carbonic gas, then the process of donation can begin. Since the dead person has rarely spelled out what should be done with her organs in such a situation, transplant teams have to meet the family or the relatives in order to get their consent to perform the surgery necessary to extract living organs from the dead body. his is compulsory when the legislation requires an explicit consent, but this is also practically what happens when presumed consent applies because surgeons are very anxious about possible negative reactions from grieving families, as well as public opinion concerning what might appear to be the unethical harvesting of organs. Is this a gift? he donor does not actually give her organs, since she is dead. If there is a gift, it comes from family members or relatives who give their consent to the surgery necessary to open the body and extract organs. What about inter vivos gift-giving? In that case, the person actually decides by herself to give or not to give this right to extract a kidney, a lobe of her liver or, more rarely, a part of one of her lungs. Furthermore, in most cases, longstanding afective relations between the donor and the recipients are the basis of the donation.

We may thus consider that, in this speciic case, Maussian obligations are at work: a moral obligation to give to a relative facing bad health or, worse, facing death; an obligation to receive in order to escape painful, time-consuming and costly dialysis; a moral obligation to give back to the one who chose to help you when you were in a medical distress. However, even in that case, the medical organization remains central: it is the medical staf who decides if a relative may or may not give, according to medical and psycho-sociological standards and guidelines. he medical organization remains central because the medical staf performs the surgery, and also because of the complex follow-up for both donor and recipient.

The technical dimension managed and performed by the organization is thus central in both forms of donation. There is a quantitative proof of that centrality: annual reports of the French organization in charge of the coordination of all transplant activity in the country provides information about the rejection of organs given by the relatives of brain-dead patients. These reports make clear that, contrary to what is commonly thought when a gift is under consideration, biomedical gifts are commonly discarded by the organization. In 2006 the report regretted that 31% of the families approached for a gift turned down the offer, but it also mentioned that 18% of potential donors did not become actual donors because of medical obstacles, or because of antecedent conditions in the donors. In 2011 these igures were unchanged.

Similarly, people willing to give a kidney to a relative or a spouse may be prevented from doing so if their medical status and/or socio-psychological proile do not meet the legal and medical requirements for a nephrectomy to be performed.

In France, as in most countries, living donation entails both technical and relational dimensions or, to put it in other words, legal-medical obligations and moral ones; it is thus impossible to consider only the relational and moral dimension, as a Maussian approach would suggest.

his does not just amount to saying that biomedical gifts are "a bit more complex" than usual gifts, those that one may perform in the course of daily activity. From a sociological point of view, a biomedical gift has a greater resemblance to a sacriice -a social coniguration close to a gift -than to a Maussian gift. What is the diference between a sacriice and a gift? According to the work of Mauss and his friend and colleague Henri Hubert (1898), the diference comes from the presence of a third party in the coniguration. While the gift is a performance in which the donor and the donee meet and proceed to the actualization of the irst two obligations (to give and to receive) before they meet in the future to reverse their position (the third obligation turns the donee into a donor and vice-versa), this is not the case with sacriice, because between those who expect something from it and the person or the thing sacriiced there is a third party, the sacriicer, whose technique is necessary to the eicient performance of the ritual -a very "technical" ritual in the case of organ transplantation. In the post mortem production of the organ (the "scarce resource" of this commerce) the dead person is the one to be sacriiced. Qualiied members of the medical organization are the professionals whose command over the medical technique ensures that the sacriice can be performed according to the prescribed ritual (determined in this case by medical eiciency considered as a value). he last party of this coniguration is the family, who might expect no more than a moral beneit from the "gift" -namely, the beneit related to helping an unknown human being, thus giving a meaning to the sudden and unexpected death of a relative. Sacriice represents a useful step so that we might arrive at the idea of the organizational gift.

In order to gain a full picture of the system of social relations that support transplantation as a new form of commerce among human beings a inal party to the transaction has to be considered:

the donee. Once the organ is produced, the scarce resource moves swiftly from the initial organization to the inal one, the operating theatre in which the organ is grafted into the body of the recipient. To this inal individual in the transplant relational chain, the organ appears to be a gift since it is received free of charge, in the sense that this individual does not have to meet the (very great) cost of the medical procedure. It is also a gift because the recipient cannot legally oblige anybody to give a kidney; there is no obligation beyond the moral one, even if this moral obligation is itself the result of organizational work. This inal step is the reason why the sociological deinition of sacriice is not suficient to make sense of the whole process, even if it offers the possibility of understanding the role played by professionals, and the organization in which they act.

To conclude this irst point, the role and centrality of the medical organization are so prominent that I suggest that this form of transfer of resource be considered as a genuine form of gift, an organizational gift. This is, I suggest, its main sociological characteristic. From a morphological point of view, to use a Durkheimian category, it means (see igure 1) that the production, distribution and use of the scarce resource cannot be achieved without the organizational intervention that occurs between the irst individual (the donor) and the inal individual (the recipient). When there are a stringent time constraints and a high sensitivity of the public to equity issues, medical organizations are coordinated by a "supervising organization" (UNOS in the United States, Agence de la Biomédecine in France) that may have some legal power over lower level organizations. As a consequence, the organizational gift is an "armslength gift", or a "gift at distance", compared to the direct gift theorized by Mauss. Most generally, this means that both individuals are not in direct contact, they remain "strangers" to each other.

It is worth stressing the fact that such situations are not limited to organ transplantation, and not limited to the domain of biomedicine (see, for example, [START_REF] Barman | An Institutional Approach to Donor Control: From Diadic Ties to a Field-Level Analysis[END_REF]. Basically, the organizational gift was already central in blood transfusion, from the interwar period up to the present. Some of its characteristics were explained in Titmuss' study, when he emphasized how the gift of blood differed from the gift considered by anthropologists such as Mauss and Claude Levi-Strauss (Titmuss 1970, p. 276-279); notably because, with organizations intervening, there is no longer a direct connection between donor and recipient.

In that sense, following Emile Durkheim's famous distinction, the organizational gift can be labeled an "organic gift": beyond the fact that they are both human beings, there is no similarity between both individuals, they can belong to highly differentiated social groups, and be connected only by the willingness of one of them to help a suffering "stranger", far removed from them. The division of labor in society applies equally to market exchange and to gift-giving behavior. However, beyond blood and organs, there are other situations in which organizations are central to the modern gift. This is the case with sex cells, a most interesting case because of the wide set of solutions -from commodiication to various forms of gift (with or without anonymity, direct or indirect gift, required counter-gift, etc.) -that result from different national regulations, or lack of regulation (Melh 2008, Théry 2010[START_REF] Almeling | Sex Cells. The medical market for eggs and sperm[END_REF].

One important point that medically-assisted procreation brings to the fore is the relational dimension of organizational gift. For several decades the anonymity of the gift was taken for granted since it was in the domain of organ transplantation: anonymity permitted the couple not to tell member of their inner circle how the children were conceived. It was even possible not to say anything to the children themselves. he situation changed dramatically when same sex couples stepped in since, in that case, anonymity was meaningless on the one hand and, on the other, these couples were eager to get more information from the potential donors and more opportunity to choose the donor itted to their wish than other couples could (Almeling 2011, p. 34-5;hery 2010, p. 43-4). Last but not least, some decades after the success of medicallyassisted procreation, some of the children who had become aware of how they were conceived wished to be given information about one of their biological parents in order to ill a gap in their personal identity. his has major consequences for the organization of the donation, whether framed in terms of labor for the men selling sperm, or in terms of altruism for the women selling eggs [START_REF] Almeling | Sex Cells. The medical market for eggs and sperm[END_REF], since this means that one of the central element put forward in Titmuss' analysis does not hold: the organizational gift may require that "two steps strangers" meet. In such a situation the organizational gift forbids the usual "give and forget" solution, and induces potential relations at a remote stage. his is of consequence for all the parties and for the sociological understanding of this form of organizational gift.

Organizational gift, gift and market exchange

A gift is usually associated with reciprocity.

Where does the organizational gift stand? he notion of the organizational gift is missing from the sociological approach to the gift, as represented by Philippe Chanial's otherwise comprehensive collection of surveys of gift-giving practices (Chanial 2009). It is the same with the classic book written by Jacques Godbout and Alain Caillé (1992). To see how the organizational gift belongs to the wide domain of reciprocity it is necessary to consider how the presence of organizations modiies the situation.

he organizational gift involves two consequences:

• Two types of actor take part in an organizational gift: individual persons, and moral persons (or organizations).

• Relations exist between organizations themselves, relations that have no reason to belong to the category of gift, irrespective of the nature of the relationship between the individuals at each end of the circuit of commerce.

Modern societies are not solely constituted of persons in the sense of physical individuals, as it is the case with traditional view of the giftgiving practices occurring in archaic societies.

According to James Coleman (1982), in modern society there are two types of actor capable of intervening in the transfer of resources: physical persons on the one hand; and on the other organizations, which are moral persons. he resources over which each of these persons has control differ, and this diference has an impact upon the relations that can exist between them. Physical persons have no resources other than those that belong to them -fungible property and human capital -plus those held by persons, but which belong to their network of acquaintances formed by their family, religious connections and their social neighborhood -what Coleman called social capital. Moral persons can in general make use of all the resources accumulated in the group created for the purpose of undertaking a given activity. his distinction highlights a major asymmetry between actors, and it becomes necessary to take account of the status of the latter to understand the nature of the social relations that they maintain. I therefore propose to distinguish personal social relations from functional or impersonal social relations, or alternatively personal friendship and professional friendship. Personal relations and personal friendship arises where actors are physical persons. his does not hold for relations between organizations, or between an organization and a person. In this latter case, while the individual member of the organization is of course a physical person, that person occupies a role that can require detachment and the management of personal feelings (Hochschild 2003).

On the one hand this individual is interchangeable with other members of the same organization; and on the other, relating to clients of the organization, this individual has no reason to extend the relation with this client beyond the duration of the transaction, even if this is of long duration. his point also serves as proof of the diference between the two types of relation: if the relation between the individual member of the organization and the person addressed persists even when the irst leaves the organization and/or the second ceases to be a client, it is reasonable to think that the relationship has acquired a personal status. he two persons involved now engage according to the formula "because it's me, because it's him" with which Michel de Montaigne characterized his friendship with Etienne de la Boétie. he same sort of thing can be said for relations between two individuals holding posts in organizations, and of their relationships in the framework of market relationships between organizations: two individuals can be in a situation of professional friendship, paying attention and exchanging presents as occupants of positions that place them in a working relationship. Proof of this can be seen when one of them retires, changes employer, or position: will he receive the same attention, the same presents as his predecessor?

With this distinction in mind, it is thus possible to go beyond the typology of transfer of resource (Figure 2) set by contemporary anthropologists (Descola 2005, Testart 2007) for contrasting gift and market relations on the basis of the diference between two forms of constraint (moral and legal) and the absence or existence of personal links between the exchanging parties. he intermediary type appears when the transaction involves personal relations and legal constraints salient in the case when the good is not for sale, but the owner is willing to sell it to one of his friend who wants to buy it. his is what can be considered as an "embedded market transaction".

Adapting the typology to the framework of an "organized" society (Figure 3), I suggest how we might take account of moral persons and the relationships corresponding to them: organizational connections. his typology relates personal connections to relationships between persons; and also impersonal connections to relationships between individual members of organizationsor also between the latter and persons who are "clients", understood broadly as persons making use of relationships with organizations to obtain the resources which they need.

Relying on moral constraint, the organizational gift belongs to the domain of reciprocity, like the Maussian gift; however, it difers from the latter by the fact that there is no direct and personal link between the donor and the donee. he importance of organization within this form of solidarity may be emphasized on three grounds: tarifs and cost, power relations, and the length of relational chains.

Firstly, the issue of covering the cost of the organization is of paramount importance in organ transplantation. In the case of organ transplantation, and notably in the case of kidney transplantation, the issue of cost is easily forgotten since it is far below the cost of alternative treatment (dialysis) after the irst year -the diference is about 10,000 euros a year in favor of transplantation.

In that case, the cost seems to dwindle besides the inancial gain accruing to society whenever a kidney is grafted to a patient sufering from endstage kidney failure. Nevertheless, while donation is free, it is also costly -about 7,000 euros in Continental Europe for the procurement of a kidney. he heaviest cost comes from the medical organization itself (surgeons, operating theatres, intensive care units, drugs, follow-up, etc.) which makes transplant surgery the most expensive form of surgery according to the current French tarif, the so-called tariication à l'activité (T2A): from 15,900 euros for a kidney transplant, to 39,000 euros for liver transplant, and up to 63,000 euros for a heart and lung transplant. hese tarifs include a standard stay in an intensive care unit; however additional days are common, but costly: from 600 euros a day (heart and lung transplants), to 1,100 euros (lung-heart transplant). It is worth emphasizing that tarifs do take into account the organizational dimension of donation, since hospitals could be funded if their staf assumes co-ordinating the work of various medical organizations in a given area. So in the absence of a biomarket, there is a potential economic sociology of tarifs that can be developed which would uncover how societies deal with costs when there is no market and, consequently, when the price mechanism does not operate.

Broadening the issue of the organizational cost of organizational gifts beyond organ transplantation, one may understand the importance of this point through some data related to a nonproit association active in the ield of medical , 2008). In 2005, foundations in France spent 339 millions euros: a signiicant sum indeed, but this is only 9% of the money they received (about 3.7 billions), since more than 90% of their funds were used to cover the cost of the organization 1 and pay the wages of the non-volunteer part of their workforce. Only those organizations linked to public institutions or a private irm did better -they redistributed 89% of the money they received -because their organizational costs were paid by their host organization.

Secondly, the organizational gift introduces the issue of control and power relations. Usually, power relations are left out the picture of giftgiving behavior largely because, in the case of biological donation, there is no direct connection between the donor and the recipient. However, once the organization is treated as playing a decisive and active part in donation the picture changes, and power relations arise, as Coleman pointed out. he basic idea is simple: when organizations are taken into account, the relational 1. hese costs are associated with the origination and difusion of their programs, program follow-up, expertise, audit, the building of networks, education, conferences and meetings, publications, inancial and overhead costs (Fondation de France, 2008, p. 20). dimension of donation changes signiicantly, since the person representing the organization is not there as a mere individual but has to act according to a given script, a given task to be achieved whatever that person's feelings about the matter might be. Further, it may be the case that the organizational script entails the "management of feelings" (Hochschild 2003) as a resource for the task to be performed. Furthermore, organizations have their own speciic interests in respect of the control of given resources.

his is particularly true in the case of the bioethical domain. When the irst version of the French law on bioethics was debated in Parliament during the years 1992-94, the prime issue was to gain control over surgeons working in hospitals. Some of these surgeons had been involved in irregular allocations of organs that had prevented the collection of the usual number of organs, and it was decided to create a supervising organization able to constrain members of the medical profession active in the domain of transplantation. Beyond these irregularities, and beyond the control that lower level organizations tend to have over the scarce resource because they want to ofer them to their "own" patients, this supervising organization has among its duties a proactive stance in the production of organs. In brief, to get the greatest possible control over the organs of brain-dead people; in the words of Blanca Miranda ( 2003), then at the head of the Spanish Organización Nacional de Trasplantes, the issue was to "optimize the pool of donors". At the inter-individual level, the issue of control of a scarce resource merges with the issue of power: this was the case when a heart surgeon claimed that when a person was brain-dead in his hospital, he would explain the situation to the family and then just tell them that, if they were not aware of the wishes of the dead person, he would remove organs for transplant (Cabrol 1987, p. 163). his full-blown application of the law of presumed consent was completely at variance with the way it was used by other surgeons, asking relatives for their authorization in collecting the organs of the deceased person. In the irst case, the surgeon was bluntly claiming that he used all his symbolic power as a surgeon to gain access to organs.

Control and power relations also play a role in the domain of medically-assisted procreation. Due to the great importance given to anonymity in the case of biological donation, the organization for the collection of sperm was in France founded upon the same principle: there is no relation between the sperm donor and the couple and consequently, no possible relation between the children born as a consequence of the insemination of this sperm and the initial donor, whatever their wishes might be (hery 2010). Initially, the centers that held the sperm destroyed personal information as soon as the sperm was used; now they keep their records for 30 years but they do not give any information to children, preventing them from obtaining full access to their identity. his represents a clear airmation of the organization's power with respect to individuals in the donation chain.

Thirdly, it is important to consider the relational dimension of organizational gift-giving behavior. Viviana Zelizer's work -her proposal concerning the "circuits of commerce" (Zelizer 2005(Zelizer , 2011)), and the relational dimension of her economic sociology (Zelizer 2012) -is a useful starting point. 2 She has emphasized how important it is to consider relational work, that is how people involved in transactions mingling money and intimate relations are able to agree on subtle distinctions about their transaction. 3 According to what has been said above, the relational work to be done does not limit itself to an agreement between the two parties of a given transaction: it may be necessary to take into account previous transactions (what was the nature of the transaction thanks to which the seller got the good?) or further possible transactions (what will the buyer do with the good?), or possible other parties who might emerge in future (children born from the selling/giving of sex cells). Study of the relations arising between young women working in go-go bars in Bangkok and their foreign customers, relations that mix sexuality and money, intimate feelings and payment (Roux, 2011) may illustrate the irst point. Roux explains that these young women do not consider themselves to be prostitutes, and that the relation with their foreign male customers entails much more than sex for money, even if both are at the root of the relation usually labelled and condemned as sexual 2. See as well in this volume the chapters written by Kieran Healy and Nina Bandelj.

3. According to Zelizer there are four sets of variables to be considered: 1) speciic social tasks among groups and individuals involved; 2) a set of transactions; 3) media (i.e. legal tender, token, goods, etc.) used in these transactions; and 4) negotiated meanings. tourism. Thaï women expect their regular foreign customers to "take care" of them, that is, to be provided with steady lows of money with which they can secure a living. And they themselves do likewise, "taking care" of their customer, haggling for them in local markets, ordering in the local language in restaurants, and acting in part as "tour guides", showing to their foreign friend parts of the city and of the country which they would not otherwise have known. The Thaï language translates the English word care into kae, a linguistic move that is culturally meaningful in itself, but all the more so when one bears in mind the fact that kae also has a deep cultural meaning for Thaï people. Roux explains that most of these women come from poor villages in which their families still try to make a living. Accordingly, being in the capital city they are expected to "take care" of their relatives and to send them the money necessary for the maintenance of the family home, or provide funds for the education of the youngest children. Furthermore, payments being not strictly related to sexual intercourse, an enduring relationship is established, notably in the case of a tourist going back and forth between his mother country and Thailand. This pattern its very well into Zelizer's circuits of commerce and with the role that she assigns to culture within the economy: a monetary transfer is redeined in terms that allows both parties to have a legitimate say in what is occurring, and thus helps them to reproduce their social relationship. In other words, transactions do not occur singly and in isolation, as a unit separated from the previous transaction and that following. This is precisely what Roux demonstrates: the meaning attached to the money-sex transaction in the tourist-hostess relation in Bangkok is irmly connected to the fact that the meaning of "taking care" is aligned with the "taking care" meaning attached to the hostess-family transaction. This bears direct comparison with the gift relationship as Mauss viewed it, since for Mauss the gift is characterized by a series of gifts and counter gifts, propelled by the three obligations to give, receive and give back.

Furthermore, Zelizer's circuits of commerce are limited to face-to-face relations between persons. Curiously enough, no organization has a place in these circuits, or at least their presence is not taken into account. his is most surprising, since even in the case of transactions involving life and death, dependent people, care relations, and many intimate transactions, organizations are major actors confronting individual actors in their transactions. Of course, negotiations occur between human beings, but this is not exactly the same when one of them acts as the personiication of an organization. Consideration of the role of organizations within circuits of commerce ofers two interesting perspectives: on the one hand, they may render the negotiation more diicult -because of the rigidity of organizational scripts and administrative routines which may be beyond the reach of the person involved in the face-to-face relationship with a customer.

As an example, one may think how reluctant medical staf once were, and still are, when faced with people willing to give a kidney to a friend or to an unmarried partner. he person willing to give is submitted to a series of legal and psychological examinations to ensure that the transaction its the requirements of the organization, examination procedures which go beyond the medical requirements determining the good health of the donor. On the other hand, organizations may ofer the possibility of going beyond face-to-face relations, and link the cultural shaping of transactions at the micro-level to political decisions and political debates at the macro-level. Once transactions are properly deined culturally and routinely implemented by organizations, these cultural meanings become more stable and are more likely to be taken for granted and accepted as resources for future transactions, even for a limited period of time. he idea of a series of transactions and organizations would thus render more powerful the economic sociology of circuits of commerce. his approach is has real value, and will be of use in guiding empirical research. However, the study of organisational gifts cannot be reduced to Zelizer's conception, since that does not take into account relations between persons and organisations, and so does not take account of the characteristics of the latter. To the extent that organisations are taken into account, it is necessary to question the formation and maintenance of the connections between individuals qua representatives of the organisation, and to study how interpersonal connections and impersonal interactions combine in elaborating and maintaining the social meanings characteristic of organisational gifts.

It is important to bear in mind the fact that the organizational gift implies that impersonal and personal relationships are present in the same sequence of transactions. Such a circuit of commerce involves both market exchange and gifts. he organizational gift belongs therefore to those kinds of transaction that blur the boundaries between the sphere of market exchange and that of personal relationships. All the same, the study of organizational gifts should be expanded beyond Zelizer's conception, since she does not take into account relations between persons and organizations, and so does not take account of the characteristics of the latter. And, to the extent that organizations are taken into account, it is necessary to question the formation and maintenance of the connections between individuals qua representatives of the organization, and to study how interpersonal connections and impersonal interactions combine in elaborating and maintaining the social meanings characteristic of organizational gifts. Second, organizational gift brings to the fore the issue of a longer relational chain than the one put forward in Zelizer's work. As mentioned above, when organizations enter the realm of donation, gift-giving behaviors entail a "two-stage donation" which generally transforms the individuals located at the opposite ends of the chain into strangers. But there are situations in which this process may make it possible for the individual who is one stage removed to obtain information, or even meet the person standing at the very beginning of the relational chain. In any case, the relational chain is here longer than that studied by Zelizer.

Reciprocity and alternatives forms of exchange in modern economies

he organizational gift is of inherent interest since it opens the door to an economic sociology of modern forms of gift, notably, but not exclusively, in the burgeoning ield of biomedical commerce. However, I would like to suggest that it is also of interest because it provides an opportunity to enrich our understanding of the various arenas within which people engage in transactions, possibly involving choices between scarce goods, without actually completing a market exchange. Since Polanyi's mapping of these arenas is certainly the most often used I will start with a brief summary of his approach, cast in terms of forms of integration; then I will briely explain how it is possible to elaborate his views through a focus on modern society.

Polanyi's four types of economic integration

In Trade and Market in Early Empires Polanyi proposed a general approach to the economy. This approach has two faces: the irst concerns the famous distinction between formal and substantive views of the economy. This distinction has been subjected to extensive debate among anthropologists, and to a lesser extent among economists and sociologists: it is generally thought that the substantive approach did not survive the criticism. 4 I will not consider this debate any further, and I limit my comment to one point that has been overlooked as a consequence of the opposition of formalism to substantivism. It is important to bear in mind the fact that Polanyi himself said that in modern society the substantive and the formal views merged. This is very much the case when one is compelled to offer one's labor in the market to earn one's living; in that case, any sharp distinction between the two forms almost vanishes, leaving open the question of how a Polanyian approach to the economy may be developed in such a situation. 5 he second face of Polanyi's general approach to the economy has been subjected to much less scrutiny, and has been generally thought of as a general and robust map of the various arenas of commerce in societies, notably because his map is largely compatible with the one that Mauss ofered in the concluding parts of his Essai sur le don, when he explained that there was "an atmosphere of gift" (Mauss 1925, p. 258) with the development of social welfare. his mapping distinguishes four forms of economic integration: autarky, reciprocity, redistribution and market exchange, and claims that these four forms may be at work simultaneously in any society, even if one form may have a clear supremacy. Polanyi himself gave an empirical example of the co-existence of these four forms in his posthumously published study of the kingdom of Dahomey during the 17 th and the 18 th centuries (Polanyi 1966).

The difference between the four forms are plain: autarky means an economic entity which is able 5. According to this view, Zelizer's work can be read as an attempt to explore the "hyper-monetized" world in which the substantive and the formal approach of the economy merge (see Zelizer 2005Zelizer , 2011)). I have developed some aspects of this idea further in a previous paper (Steiner 2009).

The Organizational Gift and Sociological Approaches to Exchange 14/21

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to provide for itself all the necessary elements of its economy; reciprocity means that there exist forms of exchange which involve commerce between groups of a given society, so that group A gives something to group B, which then reciprocates; redistribution means that a central agency collects part of what is produced by economic entities, and then redistributes a portion of the resources collected back to those entities; and, inally, market exchange involves the rules of exchange and of market price, and which depends on the relation between supply and demand. Central to this mapping is a strong connection between each form of exchange (or transfer of resource) and a given institution, 6 such as the oikos or any other form of economically autonomous entity; symmetrical elements within a society, and especially families; central entities such as the State; and inally the market system, in the sense of a set of markets that generate prices free from any moral, religious or political regulation.

I do not want to challenge the mapping of the economy that Polanyi made to provide tools for anthropologists, historians and social scientists busy with archaic, historical and modern economic activity respectively. I would like to focus on the most modern forms of economic activity so that we might obtain a more precise mapping of the reciprocity form, for which organizational gift-giving is a component that has so far been overlooked.

What "reciprocity" means in a market-system society Following the point of view that links the work of Polanyi and Mauss, it is clear that gift-giving practices belong to reciprocity. However, there are several forms of gift beyond those which one might make to friends and relatives.

From these gift-giving behaviors one must single out transfers of resource that take place among members of the same family or the same household -a household, or a maisonnée according to Bourdieu's conceptualization, is made up of 6. "he institution of the economic process vests that process with unity and stability; it produces a structure with a deinite function in society; it shifts the place of the process in society, thus adding signiicance to its history; it centers interest on values, motives and policy [&] he human economy, then, is embedded and enmeshed in institutions, economic and non-economic. he inclusion of non-economic is vital (Polanyi 1957, p. 148).

several nuclear families connected by kinship and solidarity. (Piketty 2011(Piketty , p. 1073, igure I), igure I). On the other hand, sociologists interested in the micro behaviors of bequest and donation stress how strongly they are related, there being complex forms of calculation between those who have received more from their parents while young, and those who will receive more through bequest. This is especially important when there is an important dynastic asset, such as vineyard or a speciic asset related to an entrepreneurial activity (a bakery for example), transmitted to one of the siblings. 10

Finally, to these direct gifts should be added organizational gifts that represent a signiicant part of gift-giving behaviors in present-day market society. Beyond what has been said above, it is interesting to consider some data collected by the Fondation de France. In 2004, 55% of French people gave money, goods or time. The report gives some detail: "21% of the people interviewed have sent a check to an organization; 18% have directly given money to somebody asking money for herself, or for a charity organization; 14% have bought goods so that a part of the price went to charity; 2% have given through a monthly debit to their bank account, and 1% through the internet page of the organization". 11 A signiicant part of these gifts therefore involves organizations, which appears to be an inescapable part of gift-giving, and which is an issue for those in charge of charity fundraising since, according to the report, there is a tendency to give to organizations to which one has already given since they are assumed to be more reliable. The issue of trust is important, since it means that donors need to trust the organization if they are to be induced to give to a stranger. Finally, it is important to stress that all these forms of reciprocity are grounded on a speciic institution: the nuclear or extended family in the case of a direct gift and bequest; charity organizations in the case of indirect gifts. In this last case, organizations are the integrative dimension because they are linked through tariffs or set prices; 12 secondly, because they convey power relation within circuits of commerce; and, inally, because they have a speciic relational form.

Forms of integration and the relational dimensions of economic integration

he relational dimension of Polanyian mapping has been left untouched by commentators so far. he present enquiry suggests that this might be given some further consideration, and the relevant relational forms added.

he relational translation of the irst three economic integrative forms does not raise diiculties (see igure 4a): autarky and oikos are represented by an isolate in a sociograph; reciprocity grounded on the family as an institution is represented by either dyadic symmetrical links (simple reciprocity), or by asymmetrical dyadic links forming a closed chain so that, in the end, any entity gives and receive; redistribution is represented by a graph in which one entity, and only one, is singled out by a maximal degree of centrality (i.e. this entity has a degree of centrality equal to 1).

he relational translation of the last form -the market system -is missing in Polanyi's writings.

Based on the two fundamental principles of equilibrium economics, I suggest that a Walrasian and an Edgeworthian graph can be used to lesh out the relational dimension of the market system understood as an exchange of information (see Figure 4b), from which the matching of goods and person (the actual transactions) results.

In a Walrasian market participants, whether on the supply or on the demand side, are connected only to an auctioneer who is the central actor for the information graph, with a maximal degree of centrality, as in the redistribution form. However, in the market system the central entity does not collect resources that have been produced or demanded, but limits itself to centralizing 12. "Insofar as exchanges at a set rate is in question, the economy is integrated by the factors which ix that rate, not by the market mechanism" (Polanyi 1957, p. 154).

Fondation Maison des sciences de l'homme -190 avenue de France -75013 Paris -France http://www.msh-paris.fr -FMSH-WP-2014-63 information so that equilibrium prices can be achieved; then a real exchange of resources may take place in the economy according to the distribution of preferences among market actors. 13 In an Edgeworthian market, each market entity is directly connected with all the others in order to negotiate and re-negotiate the price so that, in the end, the market converges toward an equilibrium price, theoretically equal to the Walrasian equilibrium price if the number of entities involved is high enough.

How can the different forms of reciprocity mentioned above be translated into a relational graph? Let us irst consider the organizational 13. See Michio Morishima's study of the Walrasian economy, and notably his demonstration of equivalence between this approach to general equilibrium through the so-called "tatônnement" process and the arbitrage process -the latter involves direct connections between market entities (Morishima 1977). When the information issue is solved, actual lows of exchanges depends on the matching technology of units demanded and supplied. In other cases, matching may produce speciic exchanges structures, as demonstrated in the case of a inancial market (Baker 1994).

gift, the simplest form. In that case (see Figure 1), the relational translation involves a relational chain going from the individual placed at the start of the process when a gift is addressed to an organization; it then runs from this organization or series of organizations to the inal individual at the end of the chain. The second form of reciprocity that I have considered is secondary redistribution among members of the household, or the household percolation of resources. In that case, the relational dimension is composed of a series of reciprocal exchanges between generations (Elderly, Parents, Children), duplicating itself from generation to generation and, as a consequence, reinforcing their protective strength when three generations are living during the same period of time. This relational structure may further be characterized by the fact that downstream lows surpass upstream lows, because the elderly transmit more than they receive from their children and grandchildren. Finally, the bequest relational system involves the various generations that receive and give resource through successive bequests in an open and never-ending process. The percolation and bequest relational systems may be sketched in a simple form as in Figure 5. 14 hese two relational systems overlap at any given point in time, provided that the three (sometimes the four) generations are alive (Masson 2009). his means that both forms of reciprocity overlap because of the equity issue within families and because of legal rules, notably in the French law of bequest that requires that wealth donated be taken into account when it comes to determine the shares of each person entitled to the bequest. his is of great signiicance when the wealth of the past generation takes the form of vineyards that one of the children continues to cultivate (Bessières 2010).

Conclusion

All these forms of reciprocity amount to suggesting that, in our present market society, the transfer of resources outside market exchanges remains important; due to their importance in the domain of biological donation, it must be stressed that they are in no respect limited to older, even antiquated, forms of exchange.

However, reciprocity in our present market societies takes diferent routes, and is much more complicated than Mauss and Polanyi ever thought possible. Particularly important is the fact that organizations play a major role in making possible some of these forms of reciprocity. Specialization and the division of labor apply to 14. Both forms of transfer of resource are at work simultaneously in the family and the household (see Masson 2010[START_REF] Bessière | De génération en génération. Arrangements de famille dans les entreprises viticoles de Cognac[END_REF]. It is worth pointing out that percolation is an illustration of the combination of reciprocity and redistribution -since a part of the resource received in a household can come from governmental redistribution -which is a "way to gain in power" as Polanyi stated (Polanyi 1957, p. 153).

reciprocity as they do to the market system. As a result, gift-giving is heavily modiied: organizations are powerful and costly, they blur or prevent the direct relation between the donor and the donee and, inally they extend the chain running from the initiating individual to the inal recipient. his new integrative system for the transfer of resources deserves the full attention of economic sociologists.

Fondation

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Fondation

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FigureFondation

  Figure 1 : Organizational gift

Figure 2 :

 2 Figure 2 : Typology of transactions

  care. During the years2004, 2005 and 2006 the association collected respectively 1.8, 1.6 and 1.6 millions euros; and when the cost of fundraising is taken into account, the net amount available is reduced signiicantly -to 56%, 55% and 47% of these sums respectively(Avare and Eynaud 2010, p. 163). The organizational cost of raising the organization's resources is only one side of the story. A study conducted for the Fondation de France demonstrates that organizational costs represent a signiicant burden for the charity sector (Fondation de France

Figure 3 :

 3 Figure 3 : Typology of transactions involving organizations

4.

  See Hann and Hart's presentation of this famous debate (Hann and Hart 2011, chap. 4).

  10. See on this point the study on the Cognac industry (Bessière 2010), and the three family budgets examined in a recent PhD devoted to the sharing of resource among French poor households (Perrin-Heredia 2011, chap. 5).11. Fondation de France, 2004, Baromètre de la générosité en France, p. 4 (See also Sylvain Lefevre 211).

Figure 4a :

 4a Figure 4a : Relational translation of the Polanyian forms of economic integration

Figure 5 :

 5 Figure 5 : Percolation and bequest as form of economic integration

7

  These are among the most important types of gift-giving practice, including social support for those in need of inancial help during

	The Organizational Gift and Sociological Approaches to Exchange	15/21
	much greater inequality in accumulated assets
	held by those families at the top of the wealth
	distribution, developed societies are reinventing
	the 19 th century "dynastic economy". This means that the low of wealth that moves, through both	periods of economic crisis. To these direct gifts
	bequest and donation, from the older generation	one should add bequests, an important transfer of
	to its successor is steadily growing, amounting in	resources that is almost completely overlooked
	2008 to 15% of the French national income -a	by current work in economic sociology. 8 Comte
	huge increase in comparison to the 4% of 1945	is probably the only great European sociologist to
		have stressed the importance of bequests among
		the forms of transfer of resources that he consi-
		dered in the second volume of his massive Sys-
		tème de politique positive: gift-giving and mar-
		ket exchange as peaceful transfers, with conquest
		and bequest representing the violent side. 9 Howe-
		ver, gift-giving and bequests are directly linked
		in his economic sociology since they both belong
		to the domain of what Comte then called altruism
		-he actually coined the word -in contradis-
		tinction to conquest and market exchange, which
		are driven by self-regarding economic motiva-tion, or egoism in Comte's vocabulary. While
		forgotten, Comte insights are relevant to mar-
		ket society today. French data related to bequest
		and donation, whether on the macro and the
		micro levels, are supporting Comte's view on
		the importance of bequest as a form of trans-
		mission of wealth. Economists interested in
		the macroeconomic role of bequest stress that
		with the growing importance of high incomes,
		the massive inequality that they create, and the
		7. In his study of the economic habitus of Algerian people
		in the 1960's Bourdieu diferentiated the nuclear family (mé-
		nage) from the household (maisonnée): "the real entity is not
		the nuclear family but the household, a group of individu-
		als and families which disposes of an aggregation of small
		incomes, and provides a far better existence than each family
		would have if reliant on its own resources only. Group soli-
		darity gives each individual and each family security against
		material and psychological destitution" (Bourdieu 1977, p.
		100; my translation). See as well Florence Weber's work on
		this point (Weber 2002).
		8. here are obvious exceptions to this, for example, Bour-
		dieu's study of matrimonial strategies of land owning fami-
		lies (Bourdieu 1980, book II, chap. 1) and Jens Beckert
		comprehensive comparative analysis of American, French
		and German bequest legislations from the middle of the
		18 th century to the present (Beckert 2004). I have limited
		my own research in this area to 19 th century France (Steiner
		2008).
		9. See Comte 1851-1854, II, p. 155. Philippe Descola has
		recently elaborated a general typology of "schemes of attach-
		ment" between humans and non-humans. He is led to dis-
		tinguishing exchange, predation and gift on one side, pro-
		duction, transmission and protection on the other (Descola
		2005, chap. 13).
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