What is Vertical scaling and Horizontal scaling?
Vertical Scaling vs Horizontal Scaling in Snowflake
In the context of databases and cloud computing, scaling refers to the ability to increase the resources available to the system to handle increased load or performance demands. There are two main types of scaling strategies: vertical scaling and horizontal scaling. Both approaches are used to manage the performance and capacity of a system but in different ways.
1. Vertical Scaling (Scaling Up)
- Vertical scaling, also known as scaling up, involves increasing the resources of a single machine or node (e.g., upgrading the CPU, memory, storage, or disk space) to handle higher demand. Essentially, you make the existing system more powerful to process larger workloads.
- Key Characteristics of Vertical Scaling:
- Increasing the power of a single machine: You upgrade the existing hardware or virtual machine to have more resources (e.g., more CPUs, RAM, storage).
- Limitations: There is a limit to how much you can scale vertically, as there’s a maximum size for any individual machine or server. Once you hit the limit of the machine’s capacity, you can’t scale further without transitioning to horizontal scaling.
- Single-node operation: In vertical scaling, the system typically operates as a single node, so if the machine fails, the entire system may become unavailable.
- Example in Snowflake:
- Virtual Warehouses: In Snowflake, when you want to increase processing power, you can resize your virtual warehouse to use more compute resources (e.g., increasing the number of CPUs or memory).
- If you resize up a virtual warehouse, Snowflake adds more compute resources (e.g., additional CPU cores), improving performance for queries, data loading, etc.
Example of Vertical Scaling in Snowflake:
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ALTER WAREHOUSE my_warehouse SET WAREHOUSE_SIZE = ‘X-LARGE’;
This increases the compute resources (CPU, memory) allocated to the warehouse.
2. Horizontal Scaling (Scaling Out)
- Horizontal scaling, also known as scaling out, involves increasing the number of machines or nodes that are used in parallel to distribute the workload. Instead of upgrading a single machine, you add more machines (or nodes) to the system to handle more requests.
- Key Characteristics of Horizontal Scaling:
- Adding more machines/nodes: You increase the number of servers or instances to handle more traffic or data processing.
- No limits on scaling: Horizontal scaling can typically scale much further than vertical scaling. You can keep adding more nodes to distribute the load without hitting hardware limits, and it allows for better fault tolerance and high availability.
- Load distribution: Horizontal scaling distributes the processing load across multiple machines or nodes. If one node fails, others can take over the workload, providing better availability and fault tolerance.
- Example in Snowflake:
- Snowflake uses multi-cluster architecture for horizontal scaling. It can automatically add or remove clusters in a virtual warehouse to handle increased or decreased query load. This is particularly useful for handling high concurrency or workloads that vary in size.
- If one cluster is fully utilized, Snowflake can automatically start additional clusters (called multi-cluster warehouses) to distribute the load, improving performance without manual intervention.
Example of Horizontal Scaling in Snowflake:
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CREATE WAREHOUSE my_warehouse
WITH
WAREHOUSE_SIZE = ‘MEDIUM’
MIN_CLUSTER_COUNT = 1
MAX_CLUSTER_COUNT = 10
SCALING_POLICY = ‘ECONOMY’;
In this example, Snowflake can dynamically add up to 10 clusters to the warehouse to handle increased load and scale down as the demand decreases.
Key Differences Between Vertical and Horizontal Scaling:
Feature | Vertical Scaling (Scaling Up) | Horizontal Scaling (Scaling Out) |
Resource Allocation | Increases resources (CPU, memory, disk) on a single machine | Increases the number of machines (nodes) in the system |
Performance | Limited by the capacity of a single machine | Can scale indefinitely by adding more nodes |
Complexity | Simpler, as you’re only upgrading a single machine | More complex due to the need to manage multiple machines |
Fault Tolerance | Lower, as failure of the single machine affects the system | Higher, as multiple nodes can handle the workload, providing redundancy |
Cost | Can become expensive when the machine needs high resources | Can be more cost-effective at scale, since resources can be dynamically managed |
Example in Snowflake | Resizing a virtual warehouse to a larger size | Using multi-cluster warehouses for dynamic scaling |
When to Use Each Scaling Method:
- Vertical Scaling is generally useful for:
- Smaller workloads where a single machine can handle the demand.
- Quick, short-term solutions to address performance bottlenecks.
- Simpler environments where adding more hardware resources to a single machine is sufficient.
- Horizontal Scaling is generally useful for:
- Large-scale or high-concurrency environments where performance demands exceed what a single machine can handle.
- Applications requiring high availability, fault tolerance, and the ability to handle significant increases in demand.
- Cloud environments where you need the flexibility to scale out quickly and efficiently.
Summary
- Vertical Scaling (Scaling Up): Involves adding more resources (CPU, RAM, storage) to a single node or machine to improve performance. It’s a simple approach but limited by hardware capacity.
- Horizontal Scaling (Scaling Out): Involves adding more nodes or machines to handle increased load. It’s more flexible and scalable than vertical scaling and is commonly used in distributed systems, such as Snowflake’s multi-cluster virtual warehouses.
What are the diff types of tables in Snowflake?
In Snowflake, there are several types of tables, each serving different purposes depending on the use case, data structure, and performance needs. These table types are designed to handle various aspects of data storage, query performance, and use cases like temporary data, historical data, or large-scale, distributed queries. Here’s an overview of the different types of tables in Snowflake:
1. Permanent Tables
- Description:
- These are the default tables in Snowflake. They store data persistently in the database.
- The data in permanent tables is retained until explicitly deleted or dropped.
- Snowflake automatically handles the storage and metadata, making it ideal for long-term storage of structured data.
- Use Cases:
- General-purpose storage of business data, transactional data, or any data you need to persist in Snowflake.
- Example:
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CREATE TABLE my_table (
id INT,
name STRING,
created_at TIMESTAMP
);
2. Temporary Tables
- Description:
- Temporary tables store data for the duration of a session. Once the session ends, the data and the table itself are automatically dropped.
- These tables are useful for storing intermediate results that are needed only within the session in which they were created.
- Use Cases:
- Storing intermediate data during ETL processes.
- Working with temporary results in data processing workflows.
- Key Characteristics:
- Temporary tables are only visible within the session in which they are created.
- They cannot be shared across sessions or users.
- Example:
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CREATE TEMPORARY TABLE temp_table (
id INT,
value STRING
);
3. Transient Tables
- Description:
- Transient tables are similar to permanent tables but with one important difference: they do not have fail-safe data recovery (which is available for permanent tables).
- These tables are designed for use cases where you want to store data temporarily but still want it to persist beyond the session, such as when storing data that can be recomputed if lost.
- Use Cases:
- Storing temporary but important data that does not need to be recoverable through Snowflake’s fail-safe feature.
- Lower cost alternative to permanent tables when fail-safe is not necessary.
- Key Characteristics:
- Data is persistent, but Snowflake does not retain a backup copy beyond the time travel period (typically up to 1 day).
- Example:
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CREATE TRANSIENT TABLE transient_table (
id INT,
name STRING
);
4. External Tables
- Description:
- External tables allow Snowflake to query data stored outside of Snowflake (in cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage) without needing to load the data into Snowflake itself.
- These tables reference external files stored in stages and allow Snowflake to perform queries directly on the data stored externally.
- Use Cases:
- Querying large datasets stored outside Snowflake (e.g., log files, raw data dumps, external data lakes).
- Performing ad-hoc queries on external data without loading it into Snowflake.
- Key Characteristics:
- Data is stored externally, but Snowflake can query it as if it were a regular table.
- You must define a stage for the external data.
- Useful for data lakes or large unstructured data sets that are too large to import directly into Snowflake.
- Example:
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CREATE EXTERNAL TABLE external_table (
id INT,
name STRING
)
WITH LOCATION = @my_external_stage
FILE_FORMAT = (TYPE = ‘CSV’);
5. Materialized Views
- Description:
- Materialized views are special types of views that store the result of a query physically in the database for improved performance.
- Unlike regular views, which compute results on the fly when queried, materialized views precompute and store the results, which can be refreshed periodically.
- Use Cases:
- Improving query performance for complex aggregations or expensive queries.
- Useful for scenarios where data changes infrequently and you need fast access to aggregated or transformed data.
- Key Characteristics:
- Materialized views are automatically refreshed when the underlying data changes (but can be manually refreshed as well).
- You can query materialized views just like regular tables.
- Example:
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CREATE MATERIALIZED VIEW mv_example AS
SELECT id, COUNT(*) AS num_entries
FROM my_table
GROUP BY id;
6. Clustered Tables (Manual Clustering)
- Description:
- Clustered tables in Snowflake are tables that are physically organized by one or more clustering keys.
- While Snowflake’s default clustering is automatic, manual clustering allows users to specify clustering keys, which helps optimize query performance for certain types of queries.
- Use Cases:
- Improving the performance of queries with large amounts of data where specific columns are frequently queried for filtering or joins.
- Optimizing table design for large-scale data processing, where data is logically partitioned by key columns.
- Key Characteristics:
- Manual clustering requires the specification of one or more columns on which the data should be clustered.
- Snowflake will automatically manage the storage of data based on these clustering keys.
- Example:
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CREATE TABLE my_clustered_table (
id INT,
name STRING,
created_at DATE
)
CLUSTER BY (id);
7. Zero-Copy Clones (Clone Tables)
- Description:
- A Zero-Copy Clone is not a separate type of table, but rather a feature that allows users to create an exact copy of a table (or other database objects like schemas or databases) without physically duplicating the underlying data.
- When you create a clone, Snowflake creates a copy of the metadata, but the underlying data is shared, allowing for efficient storage.
- Use Cases:
- Creating development or testing environments where you want an exact copy of production data without duplicating storage.
- Creating backups or experiment environments without incurring additional storage costs.
- Key Characteristics:
- Clones are initially created with no additional storage costs because the data is not duplicated; changes are tracked and stored separately.
- Example:
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CREATE TABLE clone_table CLONE my_table;
Summary of Table Types in Snowflake
Table Type | Description | Use Case |
Permanent Table | Default table, stores data persistently until deleted. | Long-term, general-purpose storage. |
Temporary Table | Stores data only for the duration of the session. | Storing intermediate or session-specific data. |
Transient Table | Similar to permanent but without fail-safe; data can be lost after time travel period. | Temporary storage of non-critical data. |
External Table | Queries data stored outside Snowflake, in cloud storage. | Querying data from external sources like S3, Azure, or GCP. |
Materialized View | Precomputed view stored physically for faster queries. | Improving query performance for frequently run complex queries. |
Clustered Table | Tables organized by clustering keys for optimized performance. | Optimizing performance for large datasets and specific queries. |
Zero-Copy Clone | Creates a copy of the table without duplicating the underlying data. | Efficient cloning of tables for development, testing, or backup. |
Each of these table types in Snowflake has its advantages and specific use cases, allowing you to choose the right type depending on your data storage, processing, and performance needs.
What is the use of transient tables and temporary tables?
Use of Transient Tables and Temporary Tables in Snowflake
In Snowflake, both transient tables and temporary tables are designed to store data temporarily, but they differ in their characteristics, use cases, and the lifecycle of the data stored in them. Here’s a detailed breakdown of both types of tables and their uses:
1. Transient Tables
Definition:
- Transient tables are permanent tables that do not have fail-safe data protection. They store data persistently, but unlike permanent tables, Snowflake does not retain a backup copy for fail-safe beyond the time travel period (usually 1 day).
- They provide a cost-effective way to store data that does not require long-term durability and is less critical in terms of recovery (i.e., data that can be recreated if lost).
Use Cases:
- Cost-Effective Storage for Temporary Data:
- Transient tables are ideal when you need to store data temporarily but still want it to persist across sessions. Since they do not incur additional storage costs for fail-safe protection, they offer savings on storage for less-critical data.
- Intermediate or Staging Data:
- Used for storing intermediate data in ETL (Extract, Transform, Load) processes that is not needed for long-term storage but is necessary for processing.
- Data That Does Not Require Fail-Safe Protection:
- If data can be easily recomputed or is not essential for business continuity, transient tables are an ideal choice. For example, storing logs, temporary aggregations, or analytical results that do not need to be restored beyond a certain time window.
- Data that needs to be cleaned or deleted after processing:
- Transient tables are often used in cases where the data does not need to be stored permanently but should remain for the duration of a session or for some temporary processes.
Key Characteristics:
- Data in transient tables is stored persistently but is subject to time travel limits, typically a maximum of 1 day.
- No fail-safe protection beyond the time travel window.
- Data is permanent until explicitly dropped.
Example:
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CREATE TRANSIENT TABLE temp_sales_data (
id INT,
product_name STRING,
sales_amount DECIMAL(10, 2),
sales_date DATE
);
In this case, the temp_sales_data table might hold sales data for a short time during a transformation or analysis process, and it will be deleted when no longer needed.
2. Temporary Tables
Definition:
- Temporary tables are used for storing data only during the session in which they are created. These tables automatically get dropped once the session ends.
- They are typically used for intermediate data that doesn’t need to persist beyond the session and is created and used during data transformations or queries in a short-term scope.
Use Cases:
- Session-Specific Data Storage:
- Temporary tables are useful for storing intermediate or session-specific data that only needs to exist for the duration of a session. For instance, storing results of a query or data manipulations that are used within the session.
- Working with Intermediate Results:
- During complex data transformations, temporary tables help store intermediate results that are used across different parts of a session’s workflow. Once the session finishes, this data is automatically discarded.
- Testing or Development:
- Temporary tables are useful during development or testing scenarios where you may need a table structure to run some tests or simulate data without worrying about cleaning up afterward. Since they are automatically removed at the end of the session, they are great for ad-hoc testing.
- Storing Data in an ETL Pipeline:
- You can use temporary tables to hold intermediate results of an ETL pipeline. Once the pipeline is finished, you can drop the table or simply let it expire at the end of the session.
Key Characteristics:
- Session-Scoped: Temporary tables exist only for the duration of the session. Once the session ends, the table and its data are automatically dropped.
- Cannot Be Shared Across Sessions: Data in a temporary table cannot be shared with other sessions or users.
- No Data Persistence: The data is removed once the session terminates.
Example:
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CREATE TEMPORARY TABLE temp_orders (
order_id INT,
customer_id INT,
total_amount DECIMAL(10, 2),
order_date DATE
);
In this case, temp_orders will hold order data during a session, and it will automatically be dropped when the session ends, which is ideal for temporary analysis or transformations.
Key Differences Between Transient and Temporary Tables
Feature | Transient Tables | Temporary Tables |
Persistence | Data persists beyond session, but not for fail-safe recovery. | Data exists only for the duration of the session. |
Fail-Safe Protection | No fail-safe recovery (beyond time travel period). | No fail-safe protection. |
Data Availability | Data persists across sessions until explicitly dropped. | Data is session-scoped, and automatically dropped when the session ends. |
Use Case | Intermediate data, staging, and non-critical data. | Temporary data storage within a session (e.g., for ETL). |
Storage Cost | More cost-effective than permanent tables but still incurs storage costs. | Minimal cost as data is removed once the session ends. |
Data Sharing | Data is available to other sessions if not dropped. | Data cannot be shared between sessions. |
Summary of Use Cases
- Transient Tables:
- Ideal for storing temporary data that needs to persist beyond a session, but fail-safe is not required.
- Good for intermediate or staging data that will eventually be processed and deleted.
- More cost-effective than permanent tables for non-critical data.
- Temporary Tables:
- Best suited for session-specific use cases where data is only needed temporarily within a session.
- Automatically dropped at the end of the session, eliminating the need for manual cleanup.
- Perfect for testing, development, or intermediate steps in ETL processes that do not require data to persist beyond the session.
Both transient and temporary tables offer flexibility in handling data that does not need to be stored permanently, but they are used for different purposes based on how long the data should persist and whether it should be session-scoped or persist beyond the session.
What is cache layer?
Cache Layer in Snowflake
In Snowflake, the cache layer refers to a mechanism that stores frequently accessed data in memory (or a local storage cache) to speed up query performance and reduce the need to repeatedly access data from slower storage or compute resources. The cache layer improves performance by storing results of queries, intermediate steps, or metadata that is accessed frequently, allowing Snowflake to retrieve that data quickly for subsequent queries without needing to reprocess it.
Types of Caching in Snowflake
Snowflake uses several types of caching to optimize query performance:
1. Result Cache
- Description: The result cache stores the results of previously executed queries. When a query is run, Snowflake checks if the exact same query has been run before and if the underlying data hasn’t changed. If a matching query result is found in the cache, Snowflake returns the cached result instead of re-executing the query, saving time and resources.
- When It’s Used:
- This cache is used for identical queries, meaning the SQL query, the parameters, and the underlying data have not changed since the last execution.
- Duration:
- The result cache is available for 24 hours after the query is executed.
- Key Characteristics:
- It caches the final result of the query, not the entire dataset.
- If the data underlying the result changes (e.g., data is updated in a table), the cache is invalidated, and the query must be re-executed.
- Example:
- If you run the same SELECT * FROM my_table WHERE id = 100 multiple times and the underlying data hasn’t changed, Snowflake will return the cached result without needing to query the database.
2. Metadata Cache
- Description: The metadata cache stores the metadata associated with database objects such as tables, schemas, or views. When Snowflake needs to access information about database objects (e.g., table structure, column names, etc.), it can use the metadata cache to quickly retrieve this information instead of querying the system catalog each time.
- When It’s Used:
- This cache is used whenever Snowflake queries the metadata of objects, such as during DDL (Data Definition Language) operations or when querying metadata views.
- Duration:
- The metadata cache is temporary, and its content can change frequently as metadata objects are modified or dropped.
- Key Characteristics:
- Metadata is cached to speed up queries that involve checking object structures.
- This cache helps Snowflake avoid re-fetching metadata from the internal system catalog repeatedly.
- Example:
- If you query the INFORMATION_SCHEMA.TABLES view repeatedly, Snowflake can use the metadata cache to return faster results by not re-scanning the underlying system tables.
3. Virtual Warehouse Cache
- Description: The virtual warehouse cache (also known as local disk cache) stores the results of data processing operations that are performed within a virtual warehouse. Snowflake’s virtual warehouses utilize compute resources to execute queries. Frequently accessed data, intermediate steps, or join operations can be stored in local cache on the compute node to speed up future queries that involve the same data.
- When It’s Used:
- This cache is used for intermediate data during complex queries, aggregations, or joins, and can be used for subsequent operations during the same session or queries that access the same dataset.
- Duration:
- It is typically available only within the context of a query session or until the virtual warehouse is suspended.
- Key Characteristics:
- Intermediate results (such as cached joins, aggregations) can be stored temporarily on the compute node to speed up subsequent queries.
- The cache can be cleared when a virtual warehouse is suspended or when queries are executed that require different data.
- Example:
- When running a complex query with multiple aggregations, Snowflake may cache intermediate results on the compute node to avoid reprocessing the same data in future steps of the same query.
Benefits of Caching in Snowflake
- Performance Improvement:
- Reduced Query Time: Cached results allow Snowflake to serve repeated queries quickly without having to access or recompute the data from scratch.
- Faster Execution: The metadata and result cache reduce the overhead associated with accessing large datasets or computing complex queries.
- Cost Reduction:
- Reduced Compute Costs: Caching minimizes the need for expensive computational resources by reusing previously computed results. This is especially beneficial for workloads with repetitive queries.
- Efficient Resource Utilization:
- By leveraging cached data, Snowflake reduces the pressure on compute resources, which improves overall system efficiency.
- Reduced Data Transfer Latency:
- Caching frequently accessed data reduces the need to query data from remote storage or from the backend database repeatedly, which lowers latency.
Cache Invalidation
Cache invalidation happens when the cached data is no longer valid. In Snowflake:
- Result Cache Invalidates:
- When the underlying data in the table or view changes.
- When the query itself is modified (even by adding a whitespace, altering the conditions, etc.).
- Metadata Cache Invalidates:
- When the structure of an object (like a table or schema) is altered (e.g., adding/removing columns, renaming).
- Warehouse Cache Invalidates:
- When a virtual warehouse is suspended or restarted, its cache is cleared.
How to Utilize Cache Layer in Snowflake
- Monitor Query Performance: Snowflake automatically manages cache usage, but understanding its behavior can help optimize queries. You can leverage the Query Profile to analyze query performance and identify caching benefits.
- Leverage Result Cache:
- For repeated queries, ensure that the data doesn’t change between executions to benefit from the result cache.
- Snowflake’s automatic caching significantly reduces the cost of querying large datasets with unchanged results.
- Cache Usage in Multi-Cluster Warehouses:
- Snowflake’s multi-cluster architecture helps distribute compute resources across clusters and can benefit from caching data across multiple clusters to maintain consistent performance under heavy load.
Summary
- Cache Layer in Snowflake enhances query performance by storing frequently accessed data or results in memory to reduce the need for re-execution and improve efficiency.
- Types of Caching:
- Result Cache: Stores the results of entire queries for faster re-execution.
- Metadata Cache: Caches metadata (e.g., table structure) for faster access.
- Virtual Warehouse Cache: Caches intermediate results and other computational steps.
- Benefits:
- Improved query performance.
- Reduced compute costs and resource utilization.
- Faster access to frequently used data.
Snowflake’s caching capabilities help ensure that repeated or common queries run more efficiently, improving both performance and cost-effectiveness.
What are different caches available in Snowflake?
In Snowflake, caching is a critical part of the architecture designed to improve query performance and reduce compute costs. Snowflake implements several types of caches that store data, metadata, or query results to speed up processing. These caches are managed automatically by Snowflake to optimize both performance and resource usage.
Here are the different types of caches available in Snowflake:
1. Result Cache
- Description:
- The Result Cache stores the results of queries that have already been executed. When an identical query is run again (with the same SQL text, parameters, and unchanged data), Snowflake checks if the result of the query is cached. If it is, Snowflake serves the cached result, eliminating the need to execute the query again.
- When It Is Used:
- The Result Cache is used for identical queries (same SQL text and unchanged data).
- Not invalidated by changes in query execution parameters or other query characteristics (unless the data changes).
- Duration:
- Cached results remain for up to 24 hours from the time the query was executed unless the underlying data changes (e.g., tables are updated).
- Benefits:
- Faster query execution: Serving results directly from cache.
- Cost savings: Reduces unnecessary compute usage for repeated queries.
- Key Characteristics:
- Caches only the final result of the query, not the entire dataset.
- Automatically managed by Snowflake.
- Invalidates if the underlying data changes (e.g., a table update).
- Example:
- Running SELECT * FROM orders WHERE customer_id = 101 multiple times, with no changes to the orders table, will result in the query being served from the cache.
2. Metadata Cache
- Description:
- The Metadata Cache stores metadata information about database objects like tables, schemas, and views. This metadata includes the structure of the objects, column names, data types, and other structural information required to process queries.
- When It Is Used:
- Snowflake uses the metadata cache when performing operations such as querying metadata views (INFORMATION_SCHEMA), retrieving table definitions, or checking schema objects.
- Duration:
- Temporary: The metadata cache exists for as long as the metadata objects are unchanged. It can be invalidated if the metadata structure of an object is modified (e.g., a table schema change).
- Benefits:
- Faster access to metadata: Helps queries that rely on object structures (e.g., schema definitions, table structures) execute faster.
- Reduces overhead by eliminating the need to query the internal Snowflake system catalog repeatedly.
- Key Characteristics:
- Caches metadata for all database objects, such as tables and views.
- Automatically managed by Snowflake.
- Example:
- Querying INFORMATION_SCHEMA.TABLES to list all tables in a database can benefit from metadata caching to reduce lookup time.
3. Virtual Warehouse Cache (Local Cache)
- Description:
- The Virtual Warehouse Cache (also called local disk cache) is used by the virtual warehouse during query execution to store intermediate results or data processed within a query. This helps to avoid redundant operations, especially during complex queries that involve multiple steps like joins or aggregations.
- When It Is Used:
- This cache is used when executing complex queries that require intermediate processing (e.g., joins, aggregations, or filtering). Data is cached temporarily on the compute nodes to speed up query execution.
- Duration:
- This cache is only available for the duration of the query execution or the session. Once the virtual warehouse is suspended or the session ends, the cache is cleared.
- Benefits:
- Speeds up query performance by caching intermediate results.
- Reduces repeated data processing.
- Reduces I/O overhead on the virtual warehouse compute resources.
- Key Characteristics:
- Session-specific cache: Only available for the current session or query.
- Cache is automatically cleared when the virtual warehouse is suspended or a new query begins.
- Example:
- During a join operation, if Snowflake computes the intermediate result of a table join, it might store that intermediate data in the warehouse cache to speed up subsequent operations in the same query.
4. Data Cache
- Description:
- The Data Cache stores the actual data blocks (stored on disk) that are frequently accessed during query execution. It caches the data physically on the compute nodes. This means that when data is queried again, the system can directly retrieve it from the cache rather than fetching it from the storage layer.
- When It Is Used:
- This cache is used when frequent queries access the same data. Once a compute node accesses a specific block of data, it can cache the block in its local storage, reducing the need to retrieve it again from Snowflake’s storage layer.
- Duration:
- Data caching is temporary. The cache will be cleared when the virtual warehouse is suspended or the data becomes outdated.
- Benefits:
- Reduces data retrieval time by caching physical data blocks locally.
- Improves performance for repeated queries involving the same data blocks.
- Key Characteristics:
- Data block caching: Caches physical data stored in the underlying storage layer.
- Data cached at the compute level, and only accessible within the same virtual warehouse.
- Example:
- If a query frequently accesses data from a particular table, Snowflake may cache the relevant data blocks on the compute node, which speeds up subsequent queries accessing the same data.
5. Query Execution Cache
- Description:
- The Query Execution Cache stores the execution plans for queries. If the same query is executed multiple times, Snowflake can reuse the execution plan from the cache, avoiding the need to recompile the query and generate a new plan each time.
- When It Is Used:
- This cache is particularly helpful when the structure of the query is the same across executions but the data may have changed. By caching the execution plan, Snowflake can quickly execute the query without re-analyzing and optimizing it each time.
- Duration:
- Execution plans are cached until the query is altered or the underlying data changes significantly.
- Benefits:
- Faster query optimization: Reusing cached execution plans avoids the overhead of recompiling queries.
- Efficiency in reusing optimized plans across similar queries.
- Key Characteristics:
- Cached at the compute level to avoid unnecessary re-optimization of queries.
- Example:
- Running the same SELECT query multiple times on a table will benefit from cached execution plans if the query structure remains unchanged.
Summary of Caches in Snowflake
Cache Type | Purpose | When Used | Duration |
Result Cache | Caches the results of queries for repeated use. | Identical queries with unchanged data. | 24 hours (until data changes). |
Metadata Cache | Caches metadata information about objects like tables and schemas. | Queries requesting object structures. | Until metadata changes. |
Virtual Warehouse Cache | Caches intermediate results or data during query execution. | During complex queries (joins, aggregations). | Session or query duration. |
Data Cache | Caches physical data blocks from Snowflake’s storage layer. | Frequent access to the same data blocks. | Until virtual warehouse is suspended. |
Query Execution Cache | Caches execution plans for queries to speed up execution. | Repeated queries with the same structure. | Until query or data structure changes. |
Benefits of Caching in Snowflake
- Improved Performance: Faster query execution through cached results, execution plans, and data.
- Reduced Costs: Minimized compute usage by avoiding re-execution of identical queries or reprocessing of intermediate results.
- Efficiency: Optimizes resource utilization by reducing the need for repeated calculations or data retrieval from storage.
By using these caching mechanisms, Snowflake ensures that frequently accessed data and queries are executed more efficiently, ultimately leading to better performance and lower costs.
Whenever we run some query, which cache it will scan first?
When you run a query in Snowflake, the system follows a specific order for checking and utilizing caches to maximize performance. Snowflake employs a multi-tier caching mechanism to speed up query execution, and the order in which it checks caches is important.
Cache Scanning Order in Snowflake
When you run a query, Snowflake will check the caches in the following order:
1. Result Cache
- First Cache Checked:
Snowflake will first check the Result Cache to see if the result of the exact same query (with the same SQL text and unchanged underlying data) is already stored. - Conditions for Result Cache:
- The exact same query with the same parameters.
- No changes in the underlying data (e.g., table updates, inserts, deletes).
- Outcome:
- If the result is found in the cache, Snowflake immediately returns the cached result, which avoids re-executing the query and saves on compute costs and time.
- If the query is not found in the Result Cache, Snowflake will proceed to the next step.
2. Virtual Warehouse Cache (Local Cache)
- Second Cache Checked: If the result is not found in the Result Cache, Snowflake will check the Virtual Warehouse Cache. This cache stores intermediate results and data processed during the execution of queries in a specific virtual warehouse.
- Conditions for Virtual Warehouse Cache:
- Cache of intermediate results from previous steps in the query (e.g., joins, aggregations, and complex operations).
- Per-query and session-based cache, which can speed up complex queries by using cached intermediate data.
- Outcome:
- If the intermediate results or data blocks are found in the cache, Snowflake can reuse the cached data to speed up the query processing.
- If the cache is empty or invalidated, Snowflake will proceed to scan the data from storage.
3. Data Cache (Storage Cache)
- Third Cache Checked:
If the Virtual Warehouse Cache doesn’t have the required data, Snowflake will check the Data Cache. This is the cache of physical data blocks (from Snowflake’s cloud storage layer) that have been recently queried and are stored locally on the compute nodes.
- Conditions for Data Cache:
- Caches data blocks that have been accessed by the virtual warehouse.
- Data is cached locally on the compute nodes (not globally across all warehouses).
- Outcome:
- If the relevant data blocks are found in the cache, Snowflake will use them directly, speeding up access to the data.
- If the data is not available in the cache, Snowflake will fetch the data from its cloud storage (which may incur more time and costs).
4. Metadata Cache
- Last Cache Checked: If the data is not found in the Data Cache, Snowflake will check the Metadata Cache. This cache stores metadata information such as schema structure, table definitions, and column types. It is used during query planning and query execution to ensure that Snowflake understands the structure of the data it is working with.
- Conditions for Metadata Cache:
- Caches information about objects in the database, such as tables, schemas, views and other structural components.
- Outcome:
- If metadata is found in the cache, Snowflake can quickly access this information to plan the query and execute it more efficiently.
- If the metadata is not found or has changed, Snowflake will access the system catalog to retrieve the most current metadata.
Summary of Cache Scanning Order
When you run a query in Snowflake, the system scans caches in the following order:
- Result Cache: First, Snowflake checks if the query’s result has already been cached.
- Virtual Warehouse Cache: If the result isn’t cached, it checks for intermediate results or cached data from the query’s execution.
- Data Cache: If neither result nor intermediate data is found, it checks if relevant data blocks are cached locally on the compute nodes.
- Metadata Cache: Lastly, it checks the metadata cache for schema, table, and column information to optimize the query execution.
Key Points
- Snowflake’s Result Cache is the first place it checks because it’s the most efficient. If a matching result is found, it can avoid the entire query execution.
- The Virtual Warehouse Cache and Data Cache are checked next, speeding up queries by reusing intermediate or frequently accessed data.
- The Metadata Cache is last, typically to retrieve details about objects, and is usually checked during query parsing and optimization.
If none of the caches contain the required data or results, Snowflake proceeds to the cloud storage layer, fetches the data, processes it, and then may cache it for future queries.
What is failsafe zone?
Failsafe Zone in Snowflake
In Snowflake, the Failsafe Zone refers to a mechanism designed to protect and recover data in the event of unexpected failures or disasters. It’s part of Snowflake’s data protection features that ensure the availability and integrity of your data, even in cases of accidental data deletion, corruption, or other catastrophic issues.
Key Features of the Failsafe Zone:
- Data Recovery:
Failsafe provides an additional layer of data protection for your Snowflake data. Even if a user deletes data or if there’s a system failure, Failsafe ensures that the data can be recovered from backups. This can be useful if you need to restore data after a mishap that goes beyond the normal Time Travel window. - Retention Period:
- Failsafe is available after the Time Travel period expires. While Time Travel allows you to access historical versions of data (e.g., within a 1-day or 90-day window depending on your Snowflake plan), Failsafe kicks in after the Time Travel window ends.
- Typically, Failsafe lasts for 7 days after the Time Travel window expires, but it is not user-accessible. Only Snowflake’s internal support team can access and recover the data during this time.
- Use Case:
- If a critical data loss or corruption occurs (e.g., accidental deletion or corruption of data that was not backed up within the Time Travel period), the Failsafe can be used as a last-resort recovery option. However, it should be noted that Failsafe is not intended to be a primary data backup strategy—its purpose is to serve as an emergency recovery mechanism.
- Access to Failsafe Data:
- User Access: You, as a Snowflake user, cannot directly access the data stored in the Failsafe zone. If data recovery is needed, you must contact Snowflake Support, and they can restore the data from the Failsafe Zone.
- Backup and Recovery:
- Unlike Time Travel, which allows users to query historical versions of data, Failsafe is part of Snowflake’s built-in backup and recovery system and is specifically meant for disaster recovery. It provides an additional safeguard against potential catastrophic data loss.
- Data Availability:
- Snowflake guarantees data durability and availability, ensuring that even in the worst-case scenario (e.g., hardware failure, internal system errors), your data can be recovered within the Failsafe period.
Failsafe vs. Time Travel
Feature | Time Travel | Failsafe |
Purpose | Recover data within a specified window (e.g., up to 90 days) | Recovery option after Time Travel expires |
Access | User-accessible (self-service) | Snowflake Support only |
Recovery Period | Up to 1 day to 90 days (depends on your Snowflake edition) | 7 days after the Time Travel period |
Primary Use Case | Accidental data deletion or changes within a recent period | Emergency recovery from severe data loss or corruption |
Data Access | Users can query and restore data | Only Snowflake Support can restore data |
Cost | Included with Snowflake storage fees | No additional cost, part of the Snowflake platform |
Key Points to Remember:
- Failsafe is a last-resort data recovery mechanism in Snowflake.
- It is only available after the Time Travel window ends, usually for up to 7 additional days.
- It provides a safety net for recovery in case of severe data issues that cannot be recovered through Time Travel.
- Failsafe data can only be accessed by Snowflake Support, not directly by users.
Example Scenario:
If you accidentally drop a table and want to restore it within the past 30 days, you could use Time Travel. However, if you try to recover the table after 30 days have passed (or after your Time Travel retention period), you won’t be able to do so directly. But if the data is still within the Failsafe period, Snowflake’s Support team could help recover the table from the Failsafe zone.
Summary
The Failsafe Zone is an additional layer of protection in Snowflake for emergency data recovery, available after the Time Travel period expires. It ensures that Snowflake has the ability to recover your data even in the case of unexpected, catastrophic failures, but users cannot directly access it—only Snowflake Support can assist in retrieving the data.
The difference between time travel and failsafe in Snowflake?
In Snowflake, both Time Travel and Failsafe are features designed to help recover data in case of issues like accidental deletions or data corruption. However, they serve different purposes, operate under different conditions, and have varying access levels. Below is a detailed comparison between Time Travel and Failsafe:
1. Purpose
- Time Travel:
- Time Travel allows you to query and restore data from a specific point in the past within a set retention period. This is useful when you need to recover data that was accidentally deleted, updated, or changed.
- It gives users the ability to access historical versions of data for up to 90 days depending on the Snowflake edition.
- Failsafe:
- Failsafe is a last-resort mechanism that provides data recovery in case of severe data loss or corruption, typically after the Time Travel period has expired. It is designed for situations where Time Travel cannot recover the data anymore.
- It is intended for disaster recovery and is not user-accessible.
2. Access
- Time Travel:
- User-accessible: As a Snowflake user, you can query historical data or restore data directly using Time Travel. This can be done through simple SQL commands like SELECT … AT (TIMESTAMP) or UNDROP TABLE.
Failsafe:
- Snowflake Support-accessible: Failsafe data can only be restored by Snowflake Support. Users cannot directly access or query data in Failsafe. If you need data recovery beyond Time Travel, you must contact Snowflake Support to restore the data from the Failsafe zone.
3. Retention Period
- Time Travel:
- Time Travel provides a defined retention period for data recovery, which can range from 1 day to 90 days based on your Snowflake edition.
- You can access and query data from any point in that period, making it ideal for short-term recovery of accidentally modified or deleted data.
- Failsafe:
- Failsafe provides an additional 7-day recovery period after the Time Travel window ends. This 7-day period is fixed and cannot be extended.
- It kicks in only after the Time Travel retention period expires, offering a secondary chance to recover data in extreme situations.
4. Recovery Use Cases
- Time Travel:
- Accidental data changes: If you accidentally modify or delete data, you can restore it to a specific point in time.
- Query historical data: You can analyze data as it appeared at any point within the Time Travel retention period.
- Failsafe:
- Data loss beyond Time Travel: If data is lost or corrupted and cannot be recovered using Time Travel (e.g., after Time Travel has expired), Failsafe offers a secondary recovery option.
- Emergency situations: Failsafe is a disaster recovery tool for catastrophic data loss that exceeds Time Travel’s limits.
5. Data Availability
- Time Travel:
- Data is immediately accessible by the user within the Time Travel retention period. You can query or restore tables, schemas, or databases to any state during that period.
- It allows access to all types of changes (e.g., table creation, column modifications, deletions) made to the data.
- Failsafe:
- Data is only available via Snowflake Support during the Failsafe period. Once the Time Travel period ends, users cannot access Failsafe directly.
- Failsafe is a back-end backup that is used in cases where other data recovery options fail.
6. Cost
- Time Travel:
- Time Travel is included in the standard Snowflake storage costs. However, you may incur additional charges based on the amount of data stored during the Time Travel period.
- The longer the Time Travel retention period, the higher the storage cost.
- Failsafe:
- There is no additional cost for Failsafe itself, as it is a built-in feature for disaster recovery.
- However, restoring data from Failsafe requires contacting Snowflake Support, which might incur time and operational overhead.
7. Duration of Data Retention
- Time Travel:
- The duration of data retention for Time Travel depends on your Snowflake edition:
- Enterprise Edition: Retains data for up to 1 day.
- Business Critical Edition: Retains data for up to 90 days.
- Other Editions: Time Travel is configurable, but data retention is typically shorter.
- The duration of data retention for Time Travel depends on your Snowflake edition:
- Failsafe:
- Failsafe duration is 7 days after the Time Travel retention period expires. This period is fixed and cannot be adjusted.
- Data in Failsafe is not stored indefinitely.
8. Example Scenarios
Time Travel Example:
- You accidentally delete a table in Snowflake. Using Time Travel, you can restore the table to its state from yesterday, within the allowed Time Travel retention window.
Failsafe Example:
- After the Time Travel period expires, you realize that critical data was lost, and Time Travel cannot restore it anymore. In this case, you can contact Snowflake Support to restore the data from the Failsafe zone, if it’s within the Failsafe 7-day window.
Comparison Table:
Feature | Time Travel | Failsafe |
Purpose | Restore or query data within a historical window | Emergency data recovery after Time Travel expires |
Access | User-accessible (self-service) | Snowflake Support only |
Retention Period | Configurable (1 day to 90 days, depending on edition) | Fixed at 7 days after Time Travel expires |
Use Case | Accidental data modification or deletion | Recovery in case of catastrophic data loss |
Data Availability | Immediately available within Time Travel window | Only available through Snowflake Support |
Data Recovery | Query or restore historical data directly | Snowflake Support restores data |
Cost | Included with Snowflake storage charges | No additional cost |
Retention Duration | Up to 90 days (depends on edition) | 7 days after Time Travel expires |
Summary
- Time Travel: Ideal for users to recover and query data within a configurable retention period (up to 90 days), allowing self-service recovery for accidental changes or deletions.
- Failsafe: A back-end, emergency recovery mechanism used after Time Travel has expired, available only through Snowflake Support, and typically used for extreme data loss scenarios.
In essence, Time Travel is a proactive feature that allows users to recover data themselves within a set period, while Failsafe serves as a safety net when all other recovery options have been exhausted.
Where is metadata stored in Snowflake?
In Snowflake, metadata refers to information about the structure of your data, such as database schemas, tables, views, columns, file formats, users, roles, and other database objects. Snowflake manages this metadata in a way that is transparent to the user, allowing you to query and interact with your data easily.
- System Databases:
- Metadata in Snowflake is stored within Snowflake’s system databases. Specifically, it is stored in a special database called INFORMATION_SCHEMA (and other system schemas).
- Snowflake maintains several internal databases to store metadata related to all the objects, operations, and access within your account.
- INFORMATION_SCHEMA:
- Each database in Snowflake has its own INFORMATION_SCHEMA that contains metadata about that particular database, schema, and objects within it. The INFORMATION_SCHEMA is a set of views that provide details about the structure and state of objects such as tables, columns, schemas, and more.
- You can query INFORMATION_SCHEMA views to obtain metadata about your database objects. For example, to get a list of all tables in a schema, you could query the INFORMATION_SCHEMA.TABLES view.
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SELECT * FROM <database_name>.INFORMATION_SCHEMA.TABLES;
- Other System Schemas:
- SNOWFLAKE Database: Snowflake also has a SNOWFLAKE database, which contains various system-level schemas like ACCOUNT_USAGE, INFORMATION_SCHEMA, and DATABASES that hold important metadata for managing the Snowflake environment.
For example, the ACCOUNT_USAGE schema contains information about account-level objects, like user activity, query history, and warehouse usage.
- SNOWFLAKE.ACCOUNT_USAGE: Contains metadata and usage statistics related to the entire Snowflake account, including warehouse performance, user activity, and more.
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SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY;
- SNOWFLAKE.INFORMATION_SCHEMA: Contains account-level metadata, including details about users, roles, grants, and more.
- Cloud Storage (For External Tables):
- If you’re using external tables in Snowflake (tables that refer to data stored in cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage), Snowflake will store the metadata for these tables in its internal metadata layer. However, the actual data resides in the cloud storage and not in Snowflake itself.
- Metadata in Snowflake’s Internal Services:
- Snowflake also maintains internal metadata in its metadata management services. These are part of Snowflake’s microservices architecture, and they track various internal operations, such as:
- Query history
- User actions
- Access logs
- Transaction logs
- Storage usage and other internal state information.
- Snowflake also maintains internal metadata in its metadata management services. These are part of Snowflake’s microservices architecture, and they track various internal operations, such as:
This metadata is used to optimize query performance, manage access, and ensure the integrity of the database system.
Types of Metadata Stored in Snowflake
- Data Object Metadata:
- Information about data objects like tables, views, schemas, databases, and columns.
- Stored in the INFORMATION_SCHEMA of each database.
- Query Metadata:
- Metadata about the queries executed in Snowflake, including query text, execution time, query result size, and status.
- This can be found in views like QUERY_HISTORY and QUERY_HISTORY_BY_SESSION in SNOWFLAKE.ACCOUNT_USAGE.
- User and Role Metadata:
- Information about users, roles, and permissions within Snowflake.
- This is stored in the SNOWFLAKE.ACCOUNT_USAGE schema and can also be accessed through INFORMATION_SCHEMA views like USERS, ROLES, and GRANTS.
- Storage Metadata:
- Information related to storage consumption like the number of micro-partitions used, disk space for tables, and storage optimization.
- This metadata helps Snowflake manage automatic clustering and data pruning.
- Metadata for External Tables:
- When external tables reference data stored in external cloud storage, metadata related to those tables (e.g., file formats, external locations) is stored within Snowflake’s internal storage layer.
How to Access Metadata in Snowflake?
You can access metadata by querying Snowflake’s system views in the INFORMATION_SCHEMA or SNOWFLAKE.ACCOUNT_USAGE schemas.
For example:
- To view all tables in a database:
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SELECT * FROM <database_name>.INFORMATION_SCHEMA.TABLES;
- To get information about all users in the account:
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SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.USERS;
- To get query history details:
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SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY;
Summary
- Metadata in Snowflake is primarily stored in system databases, especially within the INFORMATION_SCHEMA for each database and schema.
- Snowflake also maintains other system schemas like SNOWFLAKE.ACCOUNT_USAGE to track account-level metadata such as query history, user activity, and usage statistics.
- You can query these system views to access metadata about database objects, user activity, and query performance, among other things.
- Snowflake’s metadata is crucial for managing and optimizing the performance and usage of your Snowflake environment.
This architecture ensures that Snowflake has a robust and efficient way to manage metadata for both internal use (system operations) and user queries.
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