mongodb-workload-migration

name	mongodb-workload-migration
description	Use when migrating MongoDB query or aggregation workloads into the Exasol wrapper-view plus preprocessor architecture in this repository. Covers MQL-to-SQL translation patterns, analytics-focused migration strategy, known wrapper strengths and limits, official MongoDB source patterns, and Nano validation workflow for ported workloads.

MongoDB Workload Migration

When To Use This Skill

Use this skill when the task is about:

• porting MongoDB queries into the wrapper SQL surface
• translating MQL or aggregation pipelines into Exasol SQL
• evaluating whether a MongoDB analytics workload fits the wrapper architecture
• writing migration examples, playbooks, or compatibility guidance for Mongo users
• debugging why a Mongo-style pattern feels awkward or fails on the wrapper surface

Do not use this skill for generic JSON querying unless the problem is specifically framed as a MongoDB migration or MQL translation task.

First Moves

1. Identify whether the source workload is:
   • simple document filtering
   • array-heavy analytics
   • collection joins
   • document-shaped result construction
2. Decide whether the migration target is:
   • normalized analytical SQL
   • result-shape parity with MongoDB
3. Inspect the exported MongoDB documents before talking about query translation:
   • are they plain JSON or Extended JSON (EJSON)?
   • does the payload still use MongoDB _id as a user-facing field?
   • do timestamp, ObjectId, and 64-bit integer values still appear as nested wrappers such as $date, $oid, or $numberLong?
4. Test one representative query on Nano before making broad claims about portability.

In this repository, inspect first:

• README.md
• docs/identifier-conventions.md
• structured-result-materialization-study.md
• user-studies/mongodb-focused/README.md
• tests/test_wrapper_surface.py
• tests/test_wrapper_errors.py
• tests/test_structured_result_ergonomics.py
• tests/test_structured_results_from_relational.py
• tools/wrapper_package_tool.py
• tools/structured_result_tool.py

Useful live checks:

• python3 tests/test_wrapper_surface.py
• python3 tests/test_wrapper_errors.py
• python3 tests/test_structured_result_ergonomics.py
• python3 tests/test_structured_results_from_relational.py

If your local workspace keeps the optional ignored benchmark harnesses, also run:

• python3 benchmarks/study_mongodb_migration_focus.py

Migration Mental Model

The wrapper surface is strongest when MongoDB arrays can be normalized into rows.

Think in these buckets:

• Very strong fit:

  • dotted object-field filters
  • $elemMatch
  • $unwind + $match + $group
  • multi-collection analytical joins
  • window analytics comparable to $setWindowFields
  • null-vs-missing provenance

• Good fit with shape changes:

  • $facet
  • $filter
  • $map
  • grouped customer/order history

• Weak fit today:

  • array-preserving projection as first-class output
  • automatic nested document-shaped reconstruction without switching into the structured-results-plus-TO_JSON(...) path

Important practical rule:

• If the target can be expressed as rows, joins, groups, or windows, the wrapper surface is usually a good destination.
• If the target must preserve or rebuild nested document shapes, expect more manual SQL and more migration friction.
• In this repository, that nested-output gap is now addressed by structured results plus TO_JSON(...). Use that combination when the migrated workload needs to yield nested arrays/objects again instead of plain rows.

Core Translation Table

Object fields

• Mongo: "a.b.c"
• Wrapper SQL: "a.b.c"

Array element by position

• Mongo intent: arr[0]
• Wrapper SQL: "arr[0]"
• Also supported: "arr[FIRST]", "arr[LAST]", "arr[SIZE]"

Any-element array match

• Mongo:
  • { "items.name": "x" }
• Wrapper SQL:
  • EXISTS (SELECT 1 FROM item IN row."items" WHERE item.name = 'x')

Do not use:

• "items.name"

That now fails intentionally with a wrapper error telling the user to use JOIN ... IN ... or [index].

Same-element array match ($elemMatch)

• Mongo:
  • { items: { $elemMatch: { a: 1, b: 2 } } }
• Wrapper SQL:
  • EXISTS (SELECT 1 FROM item IN row."items" WHERE item.a = 1 AND item.b = 2)

This is the right default translation for same-element semantics.

$unwind

• Mongo:
  • { $unwind: "$items" }
• Wrapper SQL:
  • JOIN item IN row."items"

For scalar arrays:

• JOIN VALUE tag IN row."tags"

$size

• Mongo:
  • { items: { $size: 2 } }
• Wrapper SQL:
  • "items[SIZE]" = 2

$lookup

• MongoDB $lookup with equality or multi-field conditions
• Wrapper SQL:
  • normal SQL JOIN
  • plus JOIN ... IN ... if one side is still nested in an array

Default strategy:

1. expand the nested side into rows
2. join on business keys using ordinary SQL predicates

$setWindowFields

• Mongo window pipelines usually map directly to SQL window functions
• Prefer normal SQL OVER (...) clauses

This is one of the wrapper architecture’s strongest migration destinations.

Repo-Specific Strengths

These are validated in this repository against Nano:

• Correlated EXISTS (SELECT 1 FROM item IN row."items" ...) works.
• Array-dot misuse such as "items.value" fails with explicit guidance.
• Mixed TYPEOF(...) and JSON_TYPEOF(...) works without hidden-column ambiguity.
• Object-array iterators support:
  • helper functions
  • iterator-rooted path/bracket traversal
• UDFs work on the wrapper surface.

See:

• user-studies/mongodb-focused/README.md

If present locally:

• benchmarks/study_mongodb_migration_focus.py

Known Boundaries To Tell The User Early

Source-document preparation matters

Tell MongoDB users early that migration is not only about query translation. It is also about preparing the exported documents so they map cleanly into the Exasol JSON Tables contract.

Default preparation guidance:

• normalize EJSON before ingest:
  • $oid -> plain string
  • $date -> ISO string or another deliberate scalar representation
  • $numberLong -> plain JSON number or string, depending on the chosen contract
• rename MongoDB _id to a business-facing field such as DOC_ID or PRODUCT_ID
• when publishing durable analytical outputs, prefer uppercase SQL-safe aliases for downstream SQL

Do not assume MongoDB field names are automatically safe as SQL-facing aliases. Names such as source, type, or value often need safer aliases on published tables or views.

Pre-Ingest Checklist For Agents

Before generating SQL or recommending ingest-and-wrap, agents should walk this checklist explicitly:

1. Is the export plain JSON?
   • if not, normalize EJSON first
2. Does the document still expose MongoDB _id?
   • if yes, rename it to a business-facing field before ingest
3. Are there obvious reserved-word field names that will later become durable SQL aliases?
   • if yes, plan safer SQL-facing aliases for published views/tables
4. Is the user asking for analytics only, or for Mongo-like nested output as well?
   • if nested output matters, plan the structured-results-plus-TO_JSON(...) path early

Concrete Preparation Examples

Treat this kind of source export:

{
  "_id": {"$oid": "507f1f77bcf86cd799439011"},
  "created_at": {"$date": "2026-01-01T00:00:00Z"},
  "visits": {"$numberLong": "42"}
}

as migration input that still needs normalization.

Preferred normalized form:

{
  "doc_id": "507f1f77bcf86cd799439011",
  "created_at": "2026-01-01T00:00:00Z",
  "visits": 42
}

That is the shape agents should prefer before recommending ingest-and-wrap.

Agent Default Recommendations For MongoDB Prep

When the user says “I exported this from MongoDB, what should I do first?”, the default answer should be:

1. convert EJSON wrappers to plain JSON scalars
2. rename _id to a business-facing field name
3. ingest that normalized JSON
4. use wrapper SQL for analytics
5. use structured results plus TO_JSON(...) only when final nested output must be rebuilt

Agents should not jump directly into query translation until steps 1 and 2 are settled.

Result shape divergence

MongoDB often returns nested arrays/documents from aggregation stages.

The wrapper surface naturally returns:

• rows
• grouped rows
• joined rows
• windowed rows

So for these patterns, set expectations explicitly:

• $facet
• $filter
• $map
• $reduce

The analytical logic may port well, while the result shape may not.

Important update for this repository:

• there is now a supported structured results + TO_JSON(...) path for rebuilding nested outputs inside Exasol
• use it when the user needs Mongo-like nested payloads, report objects, or API-facing shapes
• do not promise MongoDB-native pipeline semantics, but do treat structured results as the preferred way to keep shape-building in the database

VALUE iterators stay plain SQL

VALUE iterators intentionally do not support full JSON helper/path semantics.

Tell users:

• object arrays: use full wrapper helper/path syntax
• scalar arrays: use plain SQL on the scalar iterator value

Derived-table roots are still limited

Path/helper syntax does not start from derived-table roots.

Move those expressions into the inner SELECT or query the wrapper view directly.

Recommended Migration Workflow

1. Normalize the exported documents first:
   • convert EJSON wrappers to plain scalars
   • rename MongoDB _id
   • decide whether any SQL-facing published aliases should avoid reserved words
2. Start from the business intent, not the Mongo syntax.
3. Decide if the port should preserve:
   • same-element semantics
   • array cardinality
   • document-shaped output
4. Translate arrays first:
   • single element -> bracket access
   • any/same element -> correlated EXISTS
   • full traversal -> JOIN ... IN ...
5. Translate joins second:
   • normalize array side into rows
   • use ordinary SQL joins for collection-to-collection logic
6. Translate output shape last.
   • if rows are acceptable, stop there
   • if nested output is required, switch to the structured-results workflow and plan to emit the final payload with TO_JSON(...)

Good Default Recommendations

When a Mongo user asks “what is the SQL version of this?”:

• prefer EXISTS for $elemMatch
• prefer JOIN ... IN ... for $unwind
• prefer SQL joins for $lookup
• prefer window functions for $setWindowFields
• prefer warning about shape divergence for $facet, $filter, $map, $reduce

When a Mongo user asks for the same nested output shape:

• first decide whether the result really needs to stay nested
• if yes, use structured results rather than trying to force everything into one flat query result
• prefer the higher-level structured_shape config first
• use the one-shot preview tool to validate the shape quickly
• once the shape is installed, make TO_JSON(*) the default final outlet
• only drop to low-level synthesized_family if the shape needs exact table-family control

When an agent is generating downstream SQL objects for Mongo migrations:

• keep wrapper property references quoted
• use uppercase SQL-safe aliases for durable exported tables or views
• avoid reserved-word aliases by default
• keep natural property names only in the final TO_JSON(...) output
• explicitly surface the normalized ingested field names back to the user when _id or EJSON wrappers were rewritten upstream

What Agents Should Say Early

For MongoDB migration tasks, agents should proactively tell the user:

• Mongo query translation is only half the job; document preparation matters first
• EJSON is not the desired ingest shape; plain JSON is
• MongoDB _id should usually become a business-facing field such as doc_id
• wrapper SQL is usually a strong destination for analytics
• if the user still needs nested Mongo-like payloads, the supported final-output path is structured results plus TO_JSON(...)

What To Validate On Nano

For a serious migration task, validate all three:

1. happy-path port
   • does the translated SQL return the expected rows?
2. natural wrong first attempt
   • does Mongo-style misuse fail with a good wrapper error?
3. shape consequence
   • is the result analytically equivalent but structurally different from Mongo output?

If nested output matters, validate a fourth:

4. structured output path
   • can the migrated workload be materialized as a structured result family and emitted through TO_JSON(*)?

In this repository, the best tracked starting point is user-studies/mongodb-focused/README.md. If your local workspace also keeps the optional benchmark harnesses, benchmarks/study_mongodb_migration_focus.py is the matching executable study.

Practical Patterns To Reuse

$elemMatch

SELECT ...
FROM wrapper.root r
WHERE EXISTS (
  SELECT 1
  FROM item IN r."items"
  WHERE ...
)

$unwind + $group

SELECT item.field, COUNT(*), SUM(...)
FROM wrapper.root r
JOIN item IN r."items"
GROUP BY item.field

Multi-field $lookup

SELECT ...
FROM wrapper.left l
JOIN wrapper.right r ON ...
JOIN item IN r."items"
WHERE l.key1 = item.key1
  AND l.key2 = item.key2

$lookup + nested output

If the Mongo workload expects joined nested arrays/documents in the output:

• first write the analytical SQL that produces the needed rows
• then decide whether to:
  • keep rows,
  • or rebuild structure through structured results inside Exasol and emit it with TO_JSON(...)

$setWindowFields

WITH expanded AS (...)
SELECT ...,
       SUM(metric) OVER (
         PARTITION BY ...
         ORDER BY ...
         ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
       )
FROM expanded

Structured results for Mongo-style nested output

Use this when the user wants something closer to:

• Mongo aggregation output
• API-shaped nested documents
• report payloads with nested arrays/objects

Recommended path in this repository:

1. Start with the SQL that computes the desired root/object/array content.
2. Author a structured_shape config that describes the output hierarchy.
3. Run:

exasol-json-tables structured-results preview-json \
  --result-family-config ./dist/result_family_input.json \
  --target-schema JVS_RESULT_PREVIEW \
  --table-kind local_temporary

4. If the output shape is right, package it durably with:

exasol-json-tables structured-results package \
  --source-schema JVS_RESULT_SRC \
  --wrapper-schema JSON_VIEW_RESULT \
  --helper-schema JSON_VIEW_RESULT_INTERNAL \
  --preprocessor-schema JVS_RESULT_PP \
  --preprocessor-script JSON_RESULT_PREPROCESSOR \
  --output-dir ./dist \
  --package-name json_result \
  --result-family-config ./dist/result_family_input.json

5. Install the package, activate the preprocessor, and use TO_JSON(*) or TO_JSON("field1", "field2") as the final outlet.
6. If structured_shape is too limiting, move down to synthesized_family.

Use this especially for Mongo patterns like:

• $lookup followed by document nesting
• $facet outputs that need multiple nested arrays
• grouped results that should return arrays of child objects
• analyst or API workflows that want nested result payloads instead of rows

Tell the user clearly:

• this is not MongoDB-native output semantics
• but it is the repository’s supported way to keep structural work inside Exasol rather than rebuilding everything in application code
• preview-json is the quick validation path; TO_JSON(...) is the main final-output path once the result is wrapped

Research Anchors

Use official MongoDB docs first for source-semantics verification:

• Aggregation overview: https://www.mongodb.com/docs/manual/aggregation/
• $elemMatch: https://www.mongodb.com/docs/manual/reference/operator/query/elemmatch/
• $lookup: https://www.mongodb.com/docs/manual/reference/operator/aggregation/lookup/index.html
• $facet: https://www.mongodb.com/docs/v8.0/reference/operator/aggregation/facet/
• $filter: https://www.mongodb.com/docs/manual/reference/operator/aggregation/filter/
• $map: https://www.mongodb.com/docs/manual/reference/operator/aggregation/map/
• $reduce: https://www.mongodb.com/docs/manual/reference/operator/aggregation/reduce/
• $setWindowFields: https://www.mongodb.com/docs/v8.0/reference/operator/aggregation/setWindowFields/
• Official aggregation examples:
  • unpack arrays: https://www.mongodb.com/docs/v8.0/tutorial/aggregation-examples/unpack-arrays/
  • group and total data: https://www.mongodb.com/docs/drivers/node/v6.8/aggregation/group-total/
  • multi-field join: https://www.mongodb.com/docs/drivers/node/v5.6/aggregation-tutorials/multi-field-join/

Final Rule

Do not evaluate a MongoDB migration solely by “can SQL express this?”

Always evaluate both:

• semantic portability
• result-shape portability

This wrapper architecture is now strong on semantic portability for analytics workloads. For result shape portability:

• plain row-oriented ports are the easiest path
• structured results plus TO_JSON(...) are the supported path when nested output must stay inside Exasol
• the remaining gap is automatic Mongo-style output parity, not the total absence of a nested-output workflow