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REVIEW 3 major objections 4 minor 73 references

IDSS, a Novel P2P Relational Data Storage Service

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read IDSS claims a peer-to-peer overlay can run distributed SQL queries across every peer with no central server.

desk verdict A coherent, genuinely new P2P relational design with open-source code and honestly stated limits — but the entire empirical evaluation is one sentence and the TTL merge heuristic is uncharacterized. read the letter →

arxiv 2507.14682 v1 pith:UC7K52CB submitted 2025-07-19 cs.DB cs.DC

Massimo Cafaro , Italo Epicoco , Marco Pulimeno , Lunodzo J. Mwinuka , Lucas Pereira , Hugo Morais This is my paper · ORCID
classification cs.DBcs.DC
keywords peer-to-peerstoragedistributedqueryprocessinghashtableembeddedrelationaldatabaseSQLaggregationnestedqueriesdecentralisedbest-effortmerge
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

IDSS is a proposed storage service that combines a peer-to-peer overlay with an embedded relational database on every peer, so data can be spread across many machines with no central server. The paper's central claim is that a SQL query submitted to any peer can be broadcast through the overlay, executed locally on every peer, and merged along the reverse path back to the submitter, yielding the same relational results a single database would produce. It argues that this design supports selective queries, aggregate functions, and two-level nested queries while remaining responsive at overlay sizes from 8 to 1,024 peers. A sympathetic reader would care because this is a concrete architectural route to scalable, fault-tolerant SQL storage that avoids the scaling and single-point-of-failure problems of centralised databases.

What carries the argument

The load-bearing mechanism is the per-query state kept in every peer's QUERY table, which turns a broadcast tree into a merge tree. Each row stores the query's UQI (to recognise and discard duplicates), the sender_key (to route merged results back along the path), flags for local execution, merge completion, and failures, and a TTL computed as newTTL = oldTTL × 3/4 at each hop (Equation 1). The node waits until its TTL expires, merges the partial recordsets it has received with its own local result, and sends the combined recordset to the sender_key. For aggregate queries the same table supports a rewrite rule: replace each AVG expression with SUM and COUNT before local execution, then reconstruct the average at the initiator, which works because sum, count, min, and max are mergeable across partitions while arithmetic mean is not.

What would settle it

Deploy IDSS on a network with known per-peer row counts and submit a count(*) query with a TTL smaller than the measured time needed for the farthest peer's result to travel back; if the returned count is below the ground-truth total and the query is reported as successful, the best-effort merge is silently losing data.

Watch

Extended reading notes

Core claim

The paper sets out to prove that a fully decentralised relational data storage service is feasible: each peer runs an embedded SQL engine behind a structured one-hop DHT overlay, and every peer that receives a query executes it against a shared schema and returns partial results. Queries carry a Universal Query Identifier (UQI) so duplicate broadcasts are discarded, a sender key so each peer knows where to send merged results, and a time-to-live (TTL) that shrinks by a factor of 3/4 at each hop; each peer waits up to its TTL for children's results, merges them with its own local recordset, and forwards upstream. For aggregate queries, the paper shows that sum, count, min, and max can be merged directly, while average is handled by rewriting AVG(x) locally as SUM(x) and COUNT(x) and dividing at the initiator. Nested queries are supported up to two levels when subqueries are not correlated and are all of the same kind; the paper reports a prototype with 8 to 1,024 peers that showed a small memory footprint and remained reactive under load.

Load-bearing premise

The entire merge procedure rests on the heuristic that a time-to-live reduced to three-quarters of its value at each hop gives nodes enough time to receive and merge all partial results; if that guess is wrong for the network's size or latency, queries return incomplete answers while still appearing to have succeeded.

Editorial extensions

If this is right

  • A user can submit a SQL query to any peer and later collect the complete relational answer from it, without any node acting as a database server for the whole network.
  • Aggregate queries over all peers return the correct global sum, count, minimum, and maximum, and averages are recovered from rewritten sum/count pairs.
  • Nested queries with one level of subqueries, including aggregate subqueries, can be partitioned into elementary clauses and reassembled at the initiator.
  • The TTL value becomes an explicit accuracy/latency dial: higher TTLs give more complete results, lower TTLs answer faster but silently drop partial results.
  • The prototype's measured behaviour on 8 to 1,024 peers indicates the approach can keep a small memory footprint and stay responsive under load.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The AVG rewrite extends naturally to other aggregates expressible from sum and count, such as variance and standard deviation, though the paper does not claim this.
  • Because completeness depends on TTL, a production deployment would need a side-channel to tell users whether the returned answer is partial; the paper leaves that detection implicit.
  • A one-hop overlay means broadcast reaches every peer in a fixed number of routing steps, so the main scalability question shifts from lookup cost to the cost of merging N partial resultsets at the initiator.
  • The design assumes one shared schema; adding schema translation between peers would be a natural next step and is not addressed in the paper.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper presents IDSS, a decentralized relational data storage service that combines the D1HT one-hop distributed hash table with SQLite embedded databases. Users submit SQL queries (with a user-specified TTL) to any peer; the query is broadcast through the DHT, executed locally on all peers, and the partial results are merged along the broadcast tree toward the initiator. The paper contributes the architecture and query-processing design, including aggregate rewriting for AVG via SUM and COUNT, a state machine for asynchronous query handling, and an open-source prototype. The central claim is that IDSS provides a large-scale, fully decentralized P2P relational storage system supporting complex distributed queries without a central server.

Significance. If the architecture were validated, the main value would be the demonstration that a standard embedded relational engine behind a one-hop DHT can support distributed SQL queries, including aggregates and limited nested queries, with no central coordinator. The open-source prototype and the explicit treatment of aggregate merging are positive features. However, the paper currently provides no quantitative evaluation, and its completeness guarantee rests on an unvalidated TTL heuristic, so the significance as a systems paper is not yet established. The contribution relative to prior P2P database work (PeerDB, PORDaS, XPeer) is largely incremental: the specific combination of D1HT, SQLite, and a tree-based merge protocol is new, but no comparison is made to these systems.

major comments (3)
  1. [§3.2.3, Eq. (1)] The TTL decay rule newTTL = oldTTL × 3/4 is introduced as a heuristic ('we believe provides enough time'), and the text immediately concedes that low TTL values cause intermediate results to be silently discarded. No bound or measurement justifies the factor 3/4. Concretely, even under optimistic assumptions, the merge window for a node at depth i is T·(3/4)^i − T·(3/4)^(i+1) = T·(3/4)^i/4, while a child at depth i+1 needs at least one round trip of two one-way latencies to return its result; hence for any fixed T there exists a depth beyond which child results cannot arrive within the window. This undermines the central claim that queries are executed 'across all peers' (Abstract). The paper should either provide a TTL selection rule that accounts for tree depth and network latency, or explicitly design the system to detect and report incompleteness (e.g., an INCOMPLETE state in Figure 4) instead of treating the merge as normally COMPLETED.
  2. [§3.3] The only performance evidence is the sentence 'The results show that IDSS exhibits a small memory footprint and is reactive even under high loads (considering the stress test conditions).' No protocol, no measurements, no error bars, and no comparison to any baseline. Since the paper claims 'large-scale' and 'efficient' data management, this omission is load-bearing. The authors mention testing overlay sizes from 8 to 1024 peers; these experiments should be reported with concrete metrics (query latency, memory footprint, merge completeness, TTL sensitivity) and sufficient detail to reproduce them.
  3. [§3.2.6] The description of nested query execution (Figure 6) is too high-level to establish correctness. The paper states that the initiator obtains a recordset 'broader than that produced by the initial query' and 'all the information needed to retrieve the final recordset,' but it does not prove that fetching the parent query without its WHERE clause, combined with the global aggregate from the subquery, yields exactly the nested-query answer when data are partitioned across peers. There is also no treatment of duplicate elimination if the broadcast tree delivers multiple copies of the same tuple. This point needs either a correctness proof or a complete worked example with schema and data.
minor comments (4)
  1. [§3.1 vs Table 1] The UQI acronym is defined as 'Universal Query Identifier' in Section 3.1 but as 'Uniform Query Identifier' in Table 1; pick one definition and use it consistently.
  2. [References] The reference list contains several LaTeX artifacts and incomplete metadata, e.g., 'Nambiar and Mundra (2022,?)' and the 'Bod´ o' author name; a copyediting pass is needed.
  3. [Figure 2] The broadcast and duplicate-discard scenario is described in the body text, but the figure does not label nodes aa1, cc3, and dd4 or indicate the first-arrival order, which makes the example hard to follow.
  4. [§3.2.3] The statement that 'the TTL value should be directly proportional to the number of records expected to be retrieved (linear relationship)' is presented without justification; this is an assumption and should be stated as such or supported by an argument.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the IDSS architecture and its query-merge design are self-contained; the self-citations are background or component references, and the TTL heuristic is an acknowledged validation limitation rather than a fitted input.

full rationale

Walking the paper's derivation chain, I find no step in which a claimed result reduces by construction to an input or to a self-citation. The only formal rule, Eq. (1) in Sec. 3.2.3, defines a per-hop waiting window as newTTL = oldTTL x 3/4; the paper states explicitly that this is a heuristic (“Setting x = 3/4 is a heuristic approach that we believe provides enough time for a peer to complete its merge operations and to forward the results back”). It is an input parameter chosen by the user/designer, not a quantity fitted to query outputs, so the fitted-input-called-prediction pattern does not apply. The merging rules for aggregate functions (sum, count, min, max are composable; avg is rewritten as sum/count and divided at the initiator) and the two-level nested-query decomposition are presented as design methods; they are not derived from the results they supposedly produce. The paper cites prior work by its own authors, but none of these citations is load-bearing for the central claim. In Sec. 3.3, GRelC (Aloisio, Cafaro, Fiore, and Mirto, 2004) is cited as an enabling component for transparent access to data sources; replacing or removing GRelC would not change the central claims about P2P broadcast, local execution, hierarchical merging, or nested-query handling. The other self-citations (e.g., Sundsgaard et al., 2024) support general statements about decentralization and human intervention, not the novelty or correctness of IDSS. No uniqueness theorem from prior work is imported, and no ansatz is smuggled in via citation. The strongest caveat is a correctness limitation, not circularity: Sec. 3.2.3 concedes that with low TTL values “some intermediate results provided by the nodes in the overlay will be silently discarded,” making the merge process explicitly best-effort. That is an unvalidated heuristic and a validation gap, but it is not a circular reduction of the paper's claims to its inputs. Accordingly, the score of 2 reflects only the presence of minor, non-load-bearing self-citations; the central derivation is self-contained.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central claim rests on a common schema, a best-effort TTL merge heuristic, and reliance on the correctness of D1HT and SQLite. No scientifically invented entities are introduced; UQI is a schema mechanism.

free parameters (2)
  • TTL decay factor x = 3/4 (hand-picked heuristic)
    Introduced in Eq. 1 to set waiting times at each hop during result merge. No analysis or data supports this specific value.
  • Maximum nested query depth = 2 levels (by design)
    The architecture limits nested queries to a parent plus one subquery level (Sec. 3.2.6). This constraint is required for the merge algorithm, not derived from user requirements.
assumptions (4)
  • domain assumption All peers must share the same relational schema.
    Section 3 states 'Once defined, the schema must be the same on all peers.' This makes the broadcast-merge query model work, but rules out schema heterogeneity.
  • domain assumption Best-effort TTL merging returns sufficiently complete results.
    Section 3.2.3 relies on hop-decremented TTL values to bound waits and accepts that intermediate results can be silently discarded. Completeness is assumed, not proven.
  • domain assumption D1HT and SQLite behave as correct libraries.
    Section 3.3 selects D1HT and SQLite as implementation foundations, inheriting their correctness and performance properties without independent verification in this paper.
  • domain assumption Aggregate queries can be decomposed into per-node sum/count and recombined by the initiator.
    Section 3.2.5 says sum, count, max, min are mergeable and average is reconstructed as sum divided by count. This relies on algebraic properties and on the assumption that no merged data is lost.
invented entities (1)
  • UQI (Universal Query Identifier)
    purpose: Deduplicate broadcast queries and correlate query state across peers.
    Introduced as part of the IDSS QUERY table (Sec. 3.1). It is an internal design artifact, not an independently verifiable entity.

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Cite this review

Pith. "Pith review of IDSS, a Novel P2P Relational Data Storage Service." pith.science (2026). https://pith.science/paper/UC7K52CB

@misc{pith2026250714682,
  author       = {Pith},
  title        = {Pith review of: IDSS, a Novel P2P Relational Data Storage Service},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UC7K52CB}},
  note         = {Machine review of arXiv:2507.14682}
}
read the original abstract

The rate at which data is generated has been increasing rapidly, raising challenges related to its management. Traditional database management systems suffer from scalability and are usually inefficient when dealing with large-scale and heterogeneous data. This paper introduces IDSS (InnoCyPES Data Storage Service), a novel large-scale data storage tool that leverages peer-to-peer networks and embedded relational databases. We present the IDSS architecture and its design, and provide details related to the implementation. The peer-to-peer framework is used to provide support for distributed queries leveraging a relational database architecture based on a common schema. Furthermore, methods to support complex distributed query processing, enabling robust and efficient management of vast amounts of data are presented.

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.