Snowflake/Arctic-Text2SQL-R1-7B Fine-tuned for NL2SQL++ v8

This model is a fine-tuned version of Snowflake/Arctic-Text2SQL-R1-7B on the NL2SQL++ v8 dataset with code-with-thought reasoning.

Model Details

Base Model: Snowflake/Arctic-Text2SQL-R1-7B
Task: Text-to-SQL generation
Dataset: NL2SQL++ v8 with code-with-thought reasoning
Fine-tuning Method: LoRA (Low-Rank Adaptation) with Unsloth
Quantization: 16-bit merged weights
Training Dataset Size: (21792, 1) examples
Validation Dataset Size: (1062, 1) examples

Training Configuration

output_dir: trainer_output
per_device_train_batch_size: 8
num_train_epochs: 3.0
max_steps: -1
learning_rate: 3e-05
lr_scheduler_type: SchedulerType.COSINE
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: OptimizerNames.ADAMW_TORCH_FUSED
optim_args: None
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 8
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: True
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
logging_strategy: IntervalStrategy.STEPS
logging_steps: 3
logging_first_step: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
report_to: ['wandb']
run_name: None
project: huggingface
trackio_space_id: trackio
eval_strategy: IntervalStrategy.STEPS
eval_steps: 50
eval_delay: 0
per_device_eval_batch_size: 8
prediction_loss_only: False
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: 10
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_strategy: SaveStrategy.BEST
save_steps: 50
save_on_each_node: False
save_total_limit: 2
enable_jit_checkpoint: False
push_to_hub: False
hub_token: None
hub_private_repo: None
hub_model_id: None
hub_strategy: HubStrategy.EVERY_SAVE
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
metric_for_best_model: eval_loss
greater_is_better: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 3407
data_seed: None
use_cpu: False
accelerator_config: AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False)
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: None
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_train: False
do_eval: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: 0.1
logging_dir: None
local_rank: -1
model_init_kwargs: None
chat_template_path: None
dataset_text_field: text
dataset_kwargs: None
dataset_num_proc: None
eos_token: None
pad_token: None
max_length: 1024
packing: False
packing_strategy: bfd
padding_free: False
pad_to_multiple_of: None
eval_packing: None
completion_only_loss: None
assistant_only_loss: False
loss_type: nll
activation_offloading: False
vllm_sampling_params: None
unsloth_num_chunks: -1
unsloth_logit_chunk_multiplier: None
unsloth_grpo_mini_batch: None
max_seq_length: 12500
model_name: Snowflake/Arctic-Text2SQL-R1-7B
train_batch_size: 2
val_batch_size: 1
num_epochs: 2
lora_use_rslora: True
lora_r: 64
lora_alpha: 128
lora_dropout: 0.1

Train Dataset Example

<|im_start|>system
You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>
<|im_start|>user

You are an expert in SQL++ query generation. Given a document schema and a natural language query, generate a valid SQL++ query.

Task Instructions:
- Backtick-quote field names that are reserved keywords or contain spaces/special characters.
  WRONG: SELECT value, Enrollment (K-12) ...
  RIGHT: SELECT `value`, `Enrollment (K-12)` ...

- SUBSTR is 0-based: SUBSTR(str, 0, 4) returns the first 4 characters. Use this for year extraction from date strings.
  WRONG: SUBSTR(dob, 1, 4) = '1990'
  RIGHT: SUBSTR(dob, 0, 4) = '1990'

- Only use keyspaces and fields present in the schema; do not infer array, object, or foreign-key structure unless the schema shows it.
  WRONG: UNNEST t.tags AS tag (when `tags` is a plain string in schema)
  RIGHT: WHERE t.tags = 'sports'

- Never use CAST(); it is not supported in SQL++.
  WRONG: CAST(price AS FLOAT)
  RIGHT: TO_NUMBER(price)

- Use the exact field named in the question; do not substitute a related variant.
  WRONG: question asks for `revenue`, query uses `total_sales`
  RIGHT: query uses `revenue`

- When similar fields exist, prefer the one whose name most literally matches the question; use sample values to distinguish (e.g., `type` vs `types`, `id` vs `uuid`).
  WRONG: question asks for "account type", query uses `types` (samples: [1,2,3])
  RIGHT: uses `type` (samples: ["savings","checking"])

- Prefer a direct count or pre-aggregated field over computing it from related records when one exists.
  WRONG: (SELECT COUNT(*) FROM reviews r WHERE r.product_id = p.id) >= 3
  RIGHT: WHERE p.review_count >= 3

- Wrap string fields in TO_NUMBER() before numeric aggregation or ordering.
  WRONG: AVG(p.score)  when score is stored as "8.5"
  RIGHT: AVG(TO_NUMBER(p.score))

- If one collection contains all needed fields and filters, do not join.
  WRONG: FROM orders o JOIN orders o2 ON ...
  RIGHT: FROM orders o WHERE o.status = 'shipped'

- Use DISTINCT when unique values are requested or when a join could produce duplicates.
  WRONG: SELECT c.id FROM customers c JOIN orders o ON c.id = o.customer_id
  RIGHT: SELECT DISTINCT c.id ...

- For yes/no questions, return a single existence answer, not matching rows.
  WRONG: SELECT e.name FROM employees e WHERE e.dept = 'HR'
  RIGHT: SELECT COUNT(*) > 0 FROM employees e WHERE e.dept = 'HR'

- When listing entities with no specified attribute, return the entity identifier.
  WRONG: question says "list employees", query returns SELECT e.name
  RIGHT: SELECT e.id

- No colon after FROM.
  WRONG: FROM: orders o
  RIGHT: FROM orders o

- Every alias in a statement must be unique. Couchbase does not allow the same alias to be assigned more than once, even across subqueries or when referencing the same collection.
  WRONG: SELECT * FROM orders o WHERE o.id IN (SELECT RAW o.ref_id FROM orders o WHERE ...)
  RIGHT: SELECT * FROM orders o WHERE o.id IN (SELECT RAW o2.ref_id FROM orders o2 WHERE ...)

- Match literal types to schema field types; quote string-typed fields even when values look numeric.
  WRONG: WHERE zip_code = 10001  (zip_code type is string in the schema)
  RIGHT: WHERE zip_code = "10001"

Bucket Name: 
Scope Name: 

Collection Schema:
{"`training_bucket`.`Enterprise_Content_Management_System_scope`.`audit_events`": {"Flavor": "", "properties": {"action": {"samples": ["approve"], "type": "string"}, "actor": {"properties": {"id": {"samples": ["system"], "type": "string"}, "ip": {"samples": [["145.81.168.235"]], "type": "string"}, "type": {"samples": ["system"], "type": "string"}, "user_agent": {"samples": [["Mozilla/5.0 (Linux; Android 2...."]], "type": "string"}}, "type": "object"}, "details": {"properties": {"reason": {"samples": ["policy"], "type": "string"}, "request_id": {"samples": ["0e2f332e-d56b-4d3d-9102-f035fe..."], "type": "string"}}, "type": "object"}, "event_type": {"samples": ["access"], "type": "string"}, "occurred_at": {"samples": ["2025-09-13T09:54:25.509928Z"], "type": "string"}, "resource": {"properties": {"id": {"samples": ["doc_5f485186-2698-433b-9f5a-54..."], "type": "string"}, "type": {"samples": ["acl"], "type": "string"}, "version_id": {"samples": [["docver_20cfcb5e-3038-4c74-ac37..."]], "type": "string"}}, "type": "object"}, "result": {"samples": ["denied"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`legal_holds`": {"Flavor": "", "properties": {"criteria": {"properties": {"query": {"samples": [["classification:confidential"]], "type": "string"}, "resource_ids": {"items": {"type": "string"}, "samples": [["doc_22f4c0e5-ed3a-4bd7-a092-6139e4c4b289"]], "type": "array"}, "resource_type": {"samples": ["document"], "type": "string"}}, "type": "object"}, "matter_id": {"samples": [["MAT-23820"]], "type": "string"}, "name": {"samples": ["Legal Hold Few 1690"], "type": "string"}, "placed_at": {"samples": ["2024-03-01T07:58:54.408169Z"], "type": "string"}, "placed_by": {"samples": ["user_07f2c02b-22f9-472b-ad51-e..."], "type": "string"}, "reason": {"samples": [["Discover situation fund mouth ..."]], "type": "string"}, "released_at": {"samples": [["2024-07-02T01:01:37.386318Z"]], "type": "string"}, "released_by": {"samples": [["user_4a9ed5cf-fc48-4c38-9a3d-e..."]], "type": "string"}, "status": {"samples": ["active"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`retention_policies`": {"Flavor": "", "properties": {"applies_to": {"properties": {"document_types": {"items": {"type": "string"}, "samples": [["contract"]], "type": "array"}, "folder_ids": {"items": {"type": "string"}, "samples": [["folder_148b29da-856b-4a45-9562-9ac4cbbfaec7"]], "type": "array"}, "resource_type": {"samples": ["document"], "type": "string"}}, "type": "object"}, "created_at": {"samples": ["2023-08-31T12:06:19.235806Z"], "type": "string"}, "created_by": {"samples": ["user_426cbdba-49d4-4391-a27b-f..."], "type": "string"}, "description": {"samples": [["Eat room kind film road hotel."]], "type": "string"}, "disposition": {"properties": {"action": {"samples": ["archive"], "type": "string"}, "requires_approval": {"samples": [false], "type": "boolean"}}, "type": "object"}, "legal_hold_supported": {"samples": [false], "type": "boolean"}, "name": {"samples": ["Project Retention 2068"], "type": "string"}, "retention_days": {"samples": [106], "type": "number"}, "updated_at": {"samples": ["2024-07-20T12:06:19.235806Z"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`workflow_instances`": {"Flavor": "", "properties": {"completed_at": {"samples": [["2025-09-17T19:29:14.299184Z"]], "type": "string"}, "current_state": {"samples": ["approved"], "type": "string"}, "history": {"items": {"Flavor": "", "properties": {"action": {"samples": ["approve"], "type": "string"}, "at": {"samples": ["2025-07-01T15:41:58.261439Z"], "type": "string"}, "by": {"samples": ["user_4d99f715-7264-45bd-986a-0..."], "type": "string"}, "from_state": {"samples": [], "type": "null"}, "note": {"samples": [["Doctor start plant town busine..."]], "type": "string"}, "to_state": {"samples": [["draft"]], "type": "string"}}, "type": "object"}, "samples": [[{"action": "initiate", "at": "2025-07-01T15:41:58.261439Z", "by": "user_9b9381ab-f724-484a-b114-29761abbec7f", "from_state": null, "note": null, "to_state": "draft"}]], "type": "array"}, "initiated_by": {"samples": ["user_4d99f715-7264-45bd-986a-0..."], "type": "string"}, "resource_id": {"samples": ["doc_0f860073-d9a4-4b91-9d83-85..."], "type": "string"}, "resource_type": {"samples": ["document"], "type": "string"}, "started_at": {"samples": ["2025-07-01T15:41:58.261439Z"], "type": "string"}, "tasks": {"items": {"Flavor": "", "properties": {"assignee_id": {"samples": ["group_486ef506-41d4-4044-bd89-..."], "type": "string"}, "assignee_type": {"samples": ["group"], "type": "string"}, "comments": {"samples": [["Doctor start plant town busine..."]], "type": "string"}, "completed_at": {"samples": [["2025-09-09T03:34:08.311194Z"]], "type": "string"}, "due_at": {"samples": [["2025-09-13T03:34:08.311194Z"]], "type": "string"}, "outcome": {"samples": [["approved"]], "type": "string"}, "status": {"samples": ["cancelled"], "type": "string"}, "task_id": {"samples": ["task_1d930260-99cc-45d2-a095-f..."], "type": "string"}, "type": {"samples": ["approve"], "type": "string"}}, "type": "object"}, "samples": [[{"assignee_id": "group_b73d2f30-4629-4c56-99ae-a52f1823bb26", "assignee_type": "group", "comments": null, "completed_at": null, "due_at": "2025-11-01T12:08:03.199178Z", "outcome": null, "status": "open", "task_id": "task_943990d8-6d40-4b29-b6b7-54ec36cc6f2c", "type": "records_review"}]], "type": "array"}, "workflow_id": {"samples": ["workflow_204309ec-2029-419f-a7..."], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`workflows`": {"Flavor": "", "properties": {"applies_to": {"properties": {"document_type": {"samples": [["contract"]], "type": "string"}, "resource_type": {"samples": ["document"], "type": "string"}}, "type": "object"}, "created_at": {"samples": ["2024-09-29T22:52:34.139665Z"], "type": "string"}, "created_by": {"samples": ["user_00c6a498-9be0-4e78-bef9-f..."], "type": "string"}, "description": {"samples": [["Analysis we past year allow pa..."]], "type": "string"}, "name": {"samples": ["Contract Approval"], "type": "string"}, "states": {"items": {"properties": {"is_terminal": {"type": "boolean"}, "key": {"type": "string"}, "label": {"type": "string"}}, "type": "object"}, "samples": [[{"is_terminal": false, "key": "draft", "label": "Draft"}]], "type": "array"}, "transitions": {"items": {"properties": {"allowed_role_ids": {"items": {"type": "string"}, "type": "array"}, "from_state": {"type": "string"}, "requires_approval": {"type": "boolean"}, "to_state": {"type": "string"}, "trigger": {"type": "string"}}, "type": "object"}, "samples": [[{"allowed_role_ids": ["role_admin_9077c7d8-8cee-4c1a-83fa-9ee6538404c6"], "from_state": "draft", "requires_approval": false, "to_state": "in_review", "trigger": "submit"}]], "type": "array"}, "updated_at": {"samples": ["2025-03-16T18:03:11.117044Z"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`metadata_templates`": {"Flavor": "", "properties": {"applies_to_document_types": {"items": {"type": "string"}, "samples": [["contract"]], "type": "array"}, "created_at": {"samples": ["2024-03-09T19:26:57.522780Z"], "type": "string"}, "fields": {"items": {"Flavor": "", "properties": {"allowed_values": {"items": {}, "samples": [["draft"]], "type": "array"}, "data_type": {"samples": ["boolean"], "type": "string"}, "default_value": {"samples": [["final"]], "type": "string"}, "key": {"samples": ["field_fight_0"], "type": "string"}, "label": {"samples": ["Field Fight 0"], "type": "string"}, "multi_valued": {"samples": [false], "type": "boolean"}, "required": {"samples": [false], "type": "boolean"}}, "type": "object"}, "samples": [[{"allowed_values": [], "data_type": "number", "default_value": null, "key": "field_wife_0", "label": "Field Wife 0", "multi_valued": false, "required": true}]], "type": "array"}, "name": {"samples": ["General Metadata Template 5576"], "type": "string"}, "updated_at": {"samples": ["2024-06-27T19:26:57.522780Z"], "type": "string"}, "version": {"samples": [1], "type": "number"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`document_relationships`": {"Flavor": "", "properties": {"created_at": {"samples": ["2025-01-21T05:22:51.081737Z"], "type": "string"}, "created_by": {"samples": ["user_513f88c9-e553-48cf-bf2e-e..."], "type": "string"}, "from_document_id": {"samples": ["doc_2dc61373-f4a0-4d24-9225-20..."], "type": "string"}, "properties": {"properties": {"section": {"samples": ["12.5"], "type": "string"}}, "samples": [[{"section": "12.5"}]], "type": ["object", "object"]}, "relationship_type": {"samples": ["duplicates"], "type": "string"}, "to_document_id": {"samples": ["doc_27d22a26-0fe8-4567-ad10-1d..."], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`document_versions`": {"Flavor": "", "properties": {"comment": {"samples": [["Choice still your fill."]], "type": "string"}, "created_at": {"samples": ["2025-03-11T08:06:10.015225Z"], "type": "string"}, "created_by": {"samples": ["user_306ea255-40db-4d1f-8db6-d..."], "type": "string"}, "document_id": {"samples": ["doc_3ddabd40-eac5-49b8-ad29-49..."], "type": "string"}, "file": {"properties": {"checksum_sha256": {"samples": [["03dd82f2e75c1bac8339072ad62812..."]], "type": "string"}, "file_name": {"samples": ["behind-traditional_v1.txt"], "type": "string"}, "mime_type": {"samples": ["application/pdf"], "type": "string"}, "size_bytes": {"samples": [109665], "type": "number"}}, "type": "object"}, "is_major": {"samples": [false], "type": "boolean"}, "status": {"samples": ["available"], "type": "string"}, "storage": {"properties": {"blob_ref": {"samples": ["gcs:d6696ff5-76cc-4d46-beb3-55..."], "type": "string"}, "provider": {"samples": ["azure_blob"], "type": "string"}, "uri": {"samples": [["gcs://bucket/doc_3ddabd40-eac5..."]], "type": "string"}}, "type": "object"}, "version": {"samples": [1], "type": "number"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`documents`": {"Flavor": "", "properties": {"classification": {"properties": {"categories": {"items": {}, "samples": [["hr"]], "type": "array"}, "level": {"samples": [["internal"]], "type": "string"}, "sensitivity": {"samples": [["high"]], "type": "string"}}, "type": "object"}, "content": {"properties": {"checksum_sha256": {"samples": [["1765fa3935675368eb34e335d6651c..."]], "type": "string"}, "language": {"samples": [["es"]], "type": "string"}, "mime_type": {"samples": ["application/pdf"], "type": "string"}, "size_bytes": {"samples": [78272], "type": "number"}}, "type": "object"}, "created_at": {"samples": ["2025-02-24T00:17:34.866063Z"], "type": "string"}, "current_version_id": {"samples": ["docver_3df04086-a524-4f52-82cb..."], "type": "string"}, "custom_fields": {"properties": {"cost_center": {"samples": [[1883]], "type": "number"}, "project_code": {"samples": [["PRJ-586"]], "type": "string"}, "region": {"samples": [["APAC"]], "type": "string"}}, "type": "object"}, "deleted_at": {"samples": [["2025-07-22T04:46:48.059442Z"]], "type": "string"}, "description": {"samples": [["Decision drug research why eno..."]], "type": "string"}, "document_type": {"samples": ["email"], "type": "string"}, "folder_id": {"samples": [["folder_23b0bca5-fb10-41f9-9f6a..."]], "type": "string"}, "owner_user_id": {"samples": ["user_0ba790fa-5098-493e-86f0-6..."], "type": "string"}, "source": {"properties": {"external_ref": {"samples": [["Box:45382cc3-0064-4974-9422-f9..."]], "type": "string"}, "external_system": {"samples": [["Box"]], "type": "string"}, "ingest_method": {"samples": ["api"], "type": "string"}}, "type": "object"}, "status": {"samples": ["active"], "type": "string"}, "tags": {"items": {"type": "string"}, "samples": [["approved"]], "type": "array"}, "title": {"samples": ["Almost raise drive southern pr..."], "type": "string"}, "updated_at": {"samples": ["2025-05-16T22:15:11.866063Z"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`folders`": {"Flavor": "", "properties": {"created_at": {"samples": ["2024-12-26T00:22:16.193371Z"], "type": "string"}, "deleted_at": {"samples": [["2025-02-23T10:39:09.165685Z"]], "type": "string"}, "labels": {"items": {"type": "string"}, "samples": [["confidential"]], "type": "array"}, "name": {"samples": ["Add"], "type": "string"}, "owner_user_id": {"samples": ["user_172de4de-a87f-4d40-a000-9..."], "type": "string"}, "parent_folder_id": {"samples": [["folder_6d8bd9d6-45cc-4c6f-b55b..."]], "type": "string"}, "path": {"samples": ["/folders/04061946/loss_154"], "type": "string"}, "retention_policy_id": {"samples": [["retention_07d58ff6-5b99-41b9-9..."]], "type": "string"}, "space": {"samples": [["corp"]], "type": "string"}, "updated_at": {"samples": ["2025-06-17T03:34:24.193371Z"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`acl_assignments`": {"Flavor": "", "properties": {"created_at": {"samples": ["2025-01-01T07:27:46.418655Z"], "type": "string"}, "created_by": {"samples": ["user_5a0dbfa0-c7e8-40fe-a313-8..."], "type": "string"}, "effective_from": {"samples": [["2024-12-15T07:27:46.418655Z"]], "type": "string"}, "effective_to": {"samples": [["2025-02-18T13:05:15.427090Z"]], "type": "string"}, "resource_id": {"samples": ["doc_51f32935-5402-40c0-8be5-94..."], "type": "string"}, "resource_type": {"samples": ["document"], "type": "string"}, "role_id": {"samples": ["role_admin_9077c7d8-8cee-4c1a-..."], "type": "string"}, "scope": {"samples": ["resource"], "type": "string"}, "subject_id": {"samples": ["group_4a02b4c1-0cdc-40d6-b4b4-..."], "type": "string"}, "subject_type": {"samples": ["group"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`roles`": {"Flavor": "", "properties": {"created_at": {"samples": ["2023-07-24T10:35:15.031098Z"], "type": "string"}, "description": {"samples": ["Administrative access"], "type": "string"}, "name": {"samples": ["admin"], "type": "string"}, "permissions": {"items": {"properties": {"action": {"samples": ["*"], "type": "string"}, "conditions": {"samples": [], "type": "null"}, "effect": {"samples": ["allow"], "type": "string"}, "resource": {"samples": ["*"], "type": "string"}}, "type": "object"}, "samples": [[{"action": "*", "conditions": null, "effect": "allow", "resource": "*"}]], "type": "array"}, "updated_at": {"samples": ["2023-08-14T10:35:15.031098Z"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`groups`": {"Flavor": "", "properties": {"created_at": {"samples": ["2024-05-14T14:12:01.100683Z"], "type": "string"}, "description": {"samples": [["Brother finally high across li..."]], "type": "string"}, "member_group_ids": {"items": {"type": "string"}, "samples": [["group_2d6df9d3-ab76-44f3-99e4-dc7c0cd77136"]], "type": "array"}, "member_user_ids": {"items": {"type": "string"}, "samples": [["user_03f8130d-ce4e-4047-ac72-63e96365d443"]], "type": "array"}, "name": {"samples": ["All Employees"], "type": "string"}, "updated_at": {"samples": ["2024-08-24T14:12:01.100683Z"], "type": "string"}}, "type": "object"}, "`training_bucket`.`Enterprise_Content_Management_System_scope`.`users`": {"Flavor": "", "properties": {"auth": {"properties": {"external_id": {"samples": [["google|21431594-465f-4021-b26b..."]], "type": "string"}, "last_login_at": {"samples": [["2025-11-08T06:40:24.540843Z"]], "type": "string"}, "mfa_enabled": {"samples": [false], "type": "boolean"}, "provider": {"samples": ["google"], "type": "string"}}, "type": "object"}, "created_at": {"samples": ["2024-03-20T17:40:58.291923Z"], "type": "string"}, "display_name": {"samples": ["Brandy Rowe"], "type": "string"}, "email": {"samples": ["alan07@example.net"], "type": "string"}, "profile": {"properties": {"department": {"samples": [["Compliance"]], "type": "string"}, "locale": {"samples": [["de_DE"]], "type": "string"}, "phone": {"samples": [["(349)841-7775x823"]], "type": "string"}, "timezone": {"samples": [["America/Los_Angeles"]], "type": "string"}, "title": {"samples": [["Analyst"]], "type": "string"}}, "type": "object"}, "status": {"samples": ["active"], "type": "string"}, "updated_at": {"samples": ["2024-06-19T16:44:39.291923Z"], "type": "string"}, "username": {"samples": ["bowenchristian6474"], "type": "string"}}, "type": "object"}}

Natural Language Query:
For each group, list how many ACL assignments it has and include the role names used; also include groups that currently have no ACL assignments.

SQL++ Query:
<|im_end|>
<|im_start|>assistant
<think>
Step 1 — Read the Question
The question asks for a per-group summary. The core intent is aggregation: count ACL assignments per group and list the role names used by those assignments. It also explicitly says to include groups that currently have no ACL assignments, which implies preserving groups even when there is no matching assignment.

Entities mentioned:
- groups
- ACL assignments
- roles

Requested output:
- group identifier
- group name
- number of ACL assignments per group
- role names associated with those assignments

Filters and constraints:
- only assignments where the subject is a group
- preserve groups without assignments

Quantifiers:
- count of ACL assignments
- distinct role names

Implicit requirements:
- because role names come from assignments, a join to roles is needed
- because all groups must appear, the group side must be preserved

Step 2 — Yes/No Collection Scan
`audit_events` -> No: it tracks audit activity, not group ACL assignment counts or role names.
`legal_holds` -> No: it tracks legal hold records, unrelated to groups, ACL assignments, and roles.
`retention_policies` -> No: it stores retention policy settings, unrelated to the requested group summary.
`workflow_instances` -> No: it stores workflow execution data, not ACL assignments or group membership.
`workflows` -> No: it defines workflow templates, unrelated to ACL assignment aggregation.
`metadata_templates` -> No: it defines metadata fields, not groups, ACL assignments, or roles.
`document_relationships` -> No: it records document links, unrelated to the question.
`document_versions` -> No: it stores file versions, not group ACL data.
`documents` -> No: it stores document metadata, not group ACL assignments or role names.
`folders` -> No: it stores folder metadata, unrelated to the requested output.
`acl_assignments` -> Yes: it directly stores ACL assignment records with subject and role identifiers needed for counting and role lookup.
`roles` -> Yes: it stores role documents whose names are needed in the output.
`groups` -> Yes: it stores group documents, which are the main entity to enumerate and preserve even when no assignments exist.
`users` -> No: it stores user profiles, unrelated to group ACL assignment counts.

Step 3 — Inventory Each Yes Collection
Full path: `training_bucket`.`Enterprise_Content_Management_System_scope`.`acl_assignments`
Holds: `resource_id` (the protected object id), `resource_type` (type of resource), `role_id` (the role granted), `scope` (assignment scope), `subject_id` (the target entity id), `subject_type` (the target entity type), `created_at`, `created_by`, `effective_from`, `effective_to`
Does NOT hold: group names, group document ids as a metadata document key, or role names. Those require joins.

Full path: `training_bucket`.`Enterprise_Content_Management_System_scope`.`roles`
Holds: document identity via metadata id, `name` (role name), `description`, `permissions`, timestamps
Does NOT hold: ACL assignment rows or group information. It can tell us role names, but not which groups they belong to.

Full path: `training_bucket`.`Enterprise_Content_Management_System_scope`.`groups`
Holds: document identity via metadata id, `name` (group name), `description`, `member_group_ids`, `member_user_ids`, timestamps
Does NOT hold: ACL assignment records or role names. It supplies the group side of the summary and the key used by assignments.

Step 4 — Assign Collection Roles
Primary: `groups` — because Step 3 shows it contains the group identity and group name, and the question asks for one row per group including groups with no ACL assignments.
Secondary: `acl_assignments` — because the primary does not contain assignment records, role ids, or the subject filter needed to count ACLs per group.
Secondary: `roles` — because neither groups nor assignments contain role names, only a role id that must be resolved.
Rejected: none of the other Yes collections contribute to the requested group-level ACL summary.

Step 5 — Map Every Required Field
Output fields:
- `META(g).id` -> `group_id`
- `g.name` -> `group_name`
- `COUNT(DISTINCT META(a).id)` -> `acl_assignment_count`
- `IFMISSINGORNULL(ARRAY_AGG(DISTINCT r.name), [])` -> `role_names`

Filter fields:
- `a.subject_type` = "group"

Join keys:
- `a.subject_id = META(g).id` — the assignment stores the target group as a string id, while the group document exposes its document id through metadata.
- `META(r).id = a.role_id` — the assignment stores the role reference as a string role id, while the role document exposes its document id through metadata.

Step 6 — Decide Join and UNNEST Strategy
Join 1: `acl_assignments` ↔ `groups`
- Relationship: each ACL assignment targets a specific group through its subject fields.
- Key: `a.subject_id = META(g).id`
- Type: RIGHT JOIN — this preserves all groups, including those with no matching assignment, which matches the requirement to include groups with no ACL assignments.
- Additional join condition: `a.subject_type = "group"` ensures only assignments whose subject is a group participate in the match.

Join 2: result ↔ `roles`
- Relationship: each ACL assignment references a role.
- Key: `META(r).id = a.role_id`
- Type: INNER JOIN — only assignments with a valid role match contribute role names, which is the structure actually used.

OUTER JOIN + WHERE NULL trap:
- The outer join is intended to preserve groups with no assignments.
- The subject-type condition is placed in the ON clause rather than WHERE, so it does not eliminate preserved groups.
- As a result, the right join still behaves as a group-preserving join, and groups without assignments survive with null assignment-side values.

Step 7 — Separate ON Conditions from WHERE
ON conditions:
- `a.subject_type = "group"` -> ON: defines which assignment rows are eligible to match groups.
- `a.subject_id = META(g).id` -> ON: defines the FK relationship between assignments and groups.
- `META(r).id = a.role_id` -> ON: defines the FK relationship between roles and assignments.

WHERE conditions:
- none are used. The subject-type restriction is in ON, not WHERE, so preserved groups are not filtered away.

Step 8 — Handle Arrays
The `roles.permissions` field and other arrays exist in the schema, but they are not used in this query.
No array field is unnested here. The role names are collected with aggregation instead of UNNEST.

Step 9 — Filters and Aggregation
WHERE conditions:
- none.

Aggregation:
- `COUNT(DISTINCT META(a).id)` — counts unique ACL assignment documents associated with each group.
- `ARRAY_AGG(DISTINCT r.name)` — gathers the distinct role names used by those assignments.
- `IFMISSINGORNULL(..., [])` — converts a missing or null role-name array into an empty array for groups with no assignments.

GROUP BY:
- `META(g).id`
- `g.name`

Step 10 — Output Shape
SELECT fields:
- `META(g).id AS group_id`
- `g.name AS group_name`
- `COUNT(DISTINCT META(a).id) AS acl_assignment_count`
- `IFMISSINGORNULL(ARRAY_AGG(DISTINCT r.name), []) AS role_names`

DISTINCT:
- not needed at the top level because grouping already produces one row per group.

ORDER BY:
- `acl_assignment_count DESC`
- `group_name`

LIMIT:
- not needed.

RAW:
- not needed.

Step 11 — Alias Usage
`a` -> `acl_assignments`
`g` -> `groups`
`r` -> `roles`

Disambiguation:
- `META(g).id` is the group document id, while `a.subject_id` is the stored foreign-key string pointing to it.
- `META(r).id` is the role document id, while `a.role_id` is the stored foreign-key string pointing to it.
- `g.name` is the group name, while `r.name` is the role name; aliasing keeps them separate in the grouped output.
</think>

The correct SQL++ query is:

<sqlpp>
SELECT META(g).`id` AS group_id, g.`name` AS group_name, COUNT(DISTINCT META(a).`id`) AS acl_assignment_count, IFMISSINGORNULL(ARRAY_AGG(DISTINCT r.`name`), []) AS role_names FROM `training_bucket`.`Enterprise_Content_Management_System_scope`.`acl_assignments` AS a RIGHT JOIN `training_bucket`.`Enterprise_Content_Management_System_scope`.`groups` AS g ON a.`subject_type` = "group" AND a.`subject_id` = META(g).`id` INNER JOIN `training_bucket`.`Enterprise_Content_Management_System_scope`.`roles` AS r ON META(r).`id` = a.`role_id` GROUP BY META(g).`id`, g.`name` ORDER BY acl_assignment_count DESC, group_name;
</sqlpp>
<|im_end|>