From c60f1e90ef4a19e86585697536a6e90143cd449a Mon Sep 17 00:00:00 2001 From: Lizy Geogy <31404069+lizygeogy@users.noreply.github.com> Date: Mon, 20 Jan 2020 16:59:51 +0530 Subject: [PATCH] Community docv1.2.0 (#1530) * Duplicating more changes from Enterprise version. * Release notes * Added release notes and changes based on Doc corrections log. * Doc archives link, property updates, and a logging change. --- docs/Images/zeppelin_2.png | Bin 54682 -> 56875 bytes docs/Images/zeppelin_3.png | Bin 219711 -> 150851 bytes docs/architecture/core_components.md | 2 +- .../structured_streaming_best_practices.md | 8 +- .../configure_launch_cluster.md | 5 +- .../property_description.md | 2 + docs/connectors/connector.md | 15 +- docs/experimental.md | 16 ++ docs/howto.md | 2 - docs/howto/connect_oss_vis_client_tools.md | 42 +++--- .../load_data_from_external_data_stores.md | 6 +- ..._data_engine_to_run_approximate_queries.md | 10 +- docs/impt_info/Important_Information_CE.pdf | Bin 82284 -> 103922 bytes docs/install/install_on_premise.md | 13 ++ docs/kubernetes.md | 20 ++- docs/monitoring/monitoring.md | 6 +- docs/prev_doc_ver.md | 3 + docs/programming_guide/scala_interpreter.md | 139 ++++++++---------- ...ted_by_installing_snappydata_on-premise.md | 4 +- .../API_Reference/apireference_guide.md | 21 +-- .../command_line_utilities/scala-cli.md | 26 ++++ docs/reference/sql_reference/exec-scala.md | 50 +++++++ docs/reference/sql_reference/grant-all.md | 21 +++ docs/reference/sql_reference/pivot.md | 3 +- docs/reference/sql_reference/revoke_all.md | 20 +++ docs/reference/sql_reference/select.md | 2 +- .../TIB_compute-ce_1.2.0_relnotes.pdf | Bin 0 -> 471227 bytes docs/release_notes/release_notes.md | 2 +- docs/sde/hac_contracts.md | 11 +- docs/sde/key_concepts.md | 10 +- docs/sde/more_examples.md | 4 +- docs/sde/running_queries.md | 24 +-- docs/sde/sample_selection.md | 2 +- docs/sde/sketching.md | 12 +- docs/sde/working_with_stratified_samples.md | 23 +-- docs/security/authorization.md | 30 +++- .../setting_up_odbc_driver-tableau_desktop.md | 1 + .../collect_debug_artifacts.md | 4 +- docs/unsupported.md | 10 ++ mkdocs.yml | 14 +- store | 2 +- 41 files changed, 389 insertions(+), 196 deletions(-) create mode 100644 docs/experimental.md create mode 100644 docs/reference/command_line_utilities/scala-cli.md create mode 100644 docs/reference/sql_reference/exec-scala.md create mode 100644 docs/reference/sql_reference/grant-all.md create mode 100644 docs/reference/sql_reference/revoke_all.md create mode 100644 docs/release_notes/TIB_compute-ce_1.2.0_relnotes.pdf create mode 100644 docs/unsupported.md diff --git a/docs/Images/zeppelin_2.png b/docs/Images/zeppelin_2.png index 08b47870afc36c7860cea3fbc970d3ac404642c6..2c1ca99892b5e7b27634940d85f3f939b997ac19 100644 GIT binary patch literal 56875 zcmdSAWmJ@H`!{MK(%z)BN_T_QARx_vbeD7^-J_&{bO{JZ3k=;YAUO<3#}JZ3!wfmp z?9uyq_J6On*IxU5KfWJW!*%9$9ew=HQodqS7Wt0%ckA?0 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zTWJ4@D*e&(GHcNK(5(JqLGa&91xy7VS%^kRzquST5_mz57UR&#*HYx0tE`-W@W*;`M|pw6=m0Np&% z^_=O}NIo(N0IL9J~!1jdWlqv<{x z;ZJm2s=Gy)t=mz_dCO1S^Kh}Lu!p9(e0Nna0LLz7c=ye`d98pTcqP1A23G_n$0HTB zA9CageWUSZVWYnoAar>cdY*cVPi)$)g7DAjEM_ddGt4GqV z#!6Up&l<_OlSyNlC4ds&O-Xr~Eifb;;y#Oa+BrxSYhOV>S7&IF;?jRD2Y+=4@D1dy zO|9j&nanvVSG?|K2=SXic?Xzdtx6ebYV5G-bdMJZJpz1+COe5*dENUg{Jkl zyj8W5D|xY|fd6~X^iX=!BE!t1$_26d&=!9e`x8BRIGG&;H`nH^bXML+ujSyWpk_Ou zlb=?8_l-Nx%}wVtuKH(@)Zh(TTJAv{w{6Ydu;pm1Z8b-FZIOQ2*GA(n!|UVd7yWM} zg??F7ar!OpZG+Plm`jE@bK&?iYQlxSbCx}QMgpOO*XYnqBA6Toq?5;}>j%?y1F z*&-OVj5dK!A5O~qtNGi4Fn%+lpnK5C>>|Aie`Ti*P#aH1Fr zMM(Uwt{~=RE{)IY;(s)gKK<{K{O`$(gn9NB92kRYKzbpC8yMUxIi7v3EnSYt&%gL_t ## Using Shared File System as Checkpoint Directory Location @@ -93,5 +94,10 @@ val newSession = snappySession.newSession() The newSession instance has a similar session level config as snappySession. !!!Note - For embedded snappy jobs, it is recommended to use a new snappy-job for each streaming query. + For embedded snappy jobs, it is recommended to use a new [snappy-job](/programming_guide/snappydata_jobs.md)for each streaming query. + + +## SnappySession used for Streaming Query should not be used by other Operations + +When running a structured streaming query, Snappy hash aggregate is disabled for that entire session. This is done because SnappyData's hash aggregate does not work along with stateful aggregation required for streaming query aggregation. A best practice is to avoid using the same SnappySession instance for other operations since the Snappy hash aggregation is disabled for the entire session. diff --git a/docs/configuring_cluster/configure_launch_cluster.md b/docs/configuring_cluster/configure_launch_cluster.md index 2bb25bae61..7aaabd0a8b 100644 --- a/docs/configuring_cluster/configure_launch_cluster.md +++ b/docs/configuring_cluster/configure_launch_cluster.md @@ -105,7 +105,6 @@ The following core properties must be set in the **conf/leads** file: | heap-size | Sets the maximum heap size for the Java VM, using SnappyData default resource manager settings.
For example, `-heap-size=8g`
It is recommended to allocate minimum **6-8 GB** of heap size per lead node. If you use the `-heap-size` option, by default SnappyData sets the critical-heap-percentage to 95% of the heap size, and the `eviction-heap-percentage` to 85.5% of the `critical-heap-percentage`.
SnappyData also sets resource management properties for eviction and garbage collection if the JVM supports them. | | | dir | Working directory of the member that contains the SnappyData Server status file and the default location for the log file, persistent files, data dictionary, and so forth. | /work | | classpath | Location of user classes required by the SnappyData Server. This path is appended to the current classpath | Appended to the current classpath | -| zeppelin.interpreter.enable=true |Enable the SnappyData Zeppelin interpreter. Refer [How to use Apache Zeppelin with SnappyData](/howto/use_apache_zeppelin_with_snappydata.md) | | | spark.executor.cores | The number of cores to use on each server. | | | spark.jars | | | @@ -118,10 +117,10 @@ localhost -dir=/opt/snappydata/data/lead -heap-size=6g ``` You can add a line for each of the Lead members that you want to launch. Typically only one. In production, you may launch two. -In the following configuration, you are specifying the [number of cores to use on each server](/best_practices/setup_cluster.md#computenoscores) as well as enabling the SnappyData Zeppelin interpreter: +In the following configuration, you are specifying the [number of cores to use on each server](/best_practices/setup_cluster.md#computenoscores): ``` -localhost -spark.executor.cores=16 -zeppelin.interpreter.enable=true +localhost -spark.executor.cores=16 ``` !!!Tip diff --git a/docs/configuring_cluster/property_description.md b/docs/configuring_cluster/property_description.md index efbbb39b4f..c1ebfaa8e9 100644 --- a/docs/configuring_cluster/property_description.md +++ b/docs/configuring_cluster/property_description.md @@ -72,6 +72,7 @@ The following list of commonly used configuration properties can be set to confi |Property|Description|Components
| |-|-|-| +|-DCHECK_EXTERNAL_TABLE_AUTHZ | Enable authorization of external tables by setting this system property to true when the cluster's security is enabled. System admin or the schema owner can grant or revoke the permissions on external tables to other users.|Lead| |-J-Dsnappydata.enable-rls|Enables the system for row level security when set to true. By default this is off. If this property is set to true, then the Smart Connector access to SnappyData fails.|Server
Lead
Locator |-J-Dsnappydata.RESTRICT_TABLE_CREATION|Applicable when security is enabled in the cluster. If true, users cannot execute queries (including DDLs and DMLs) even in their default or own schema unless cluster admin explicitly grants them the required permissions using GRANT command. The default is false. |Server
Lead
Locator| |-spark.ssl.enabled|Enables or disables Spark layer encryption. The default is false. |Lead| @@ -179,6 +180,7 @@ node-l -heap-size=4096m -spark.ui.port=9090 -locators=node-b:8888,node-a:9999 -s |-snappydata.column.batchSize |The default size of blocks to use for storage in SnappyData column and store. When inserting data into the column storage this is the unit (in bytes or k/m/g suffixes for unit) that is used to split the data into chunks for efficient storage and retrieval.
This property can also be set for each table in the `create table` DDL. Maximum allowed size is 2GB. The default is 24m.| |-snappydata.column.maxDeltaRows|The maximum number of rows that can be in the delta buffer of a column table. The size of the delta buffer is already limited by `ColumnBatchSize` property, but this allows a lower limit on the number of rows for better scan performance. So the delta buffer is rolled into the column store whichever of `ColumnBatchSize` and this property is hit first. It can also be set for each table in the `create table` DDL, else this setting is used for the `create table`| |-snappydata.hiveServer.enabled|Enables the Hive Thrift server for SnappyData.This is enabled by default when you start the cluster. Thus it adds an additional 10 seconds to the cluster startup time. To avoid this additional time, you can set the property to false.| +|snappydata.maxRetryAttemptsForWrite|Default retry of Spark tasks on failure can cause duplicates in the case of insert operations. This property can be set to **0** to avoid this scenario. Other operations, as usual, retries without causing any consistency issues.| |-snappydata.sql.hashJoinSize|The join would be converted into a hash join if the table is of size less than the `hashJoinSize`. The limit specifies an estimate on the input data size (in bytes or k/m/g/t suffixes for unit). The default value is 100MB.| |-snappydata.sql.hashAggregateSize|Aggregation uses optimized hash aggregation plan but one that does not overflow to disk and can cause OOME if the result of aggregation is large. The limit specifies the input data size (in bytes or k/m/g/t suffixes for unit) and not the output size. Set this only if there are queries that can return a large number of rows in aggregation results. The default value is set to 0 which means, no limit is set on the size, so the optimized hash aggregation is always used.| |-snappydata.sql.planCacheSize|Number of query plans that will be cached.| diff --git a/docs/connectors/connector.md b/docs/connectors/connector.md index 46bb7fd68e..b12b6c8600 100644 --- a/docs/connectors/connector.md +++ b/docs/connectors/connector.md @@ -2,19 +2,18 @@ SnappyData relies on the Spark SQL Data Sources API to parallelly load data from a wide variety of sources. Any data source or database that supports Spark to load or save state can be accessed from within SnappyData. -There is built-in support for many data sources as well as data formats. You can access data from sources such as S3, file system, HDFS, Hive, and RDB. The loaders have built-in support to handle data formats such as CSV, Parquet, ORC, Avro, JSON, and Java/Scala Objects. +There is built-in support for many data sources as well as data formats. +Built-in data sources include - Amazon S3, GCS (Google Cloud storage), Azure Blob store, file systems, HDFS, Hive metastore, RDB access using JDBC, TIBCO Data Virtualization and Pivotal GemFire. -!!!Attention - This section currently only details the advanced connectors that SnappyData introduced. Refer to the [howto](../howto.md) section for a brief description about working with [external data sources](../howto/load_data_into_snappydata_tables.md) and [some examples](../howto/load_data_from_external_data_stores.md). +SnappyData supports the following data formats: CSV, Parquet, ORC, Avro, JSON, XML and Text. -SnappyData provides a utility to deploy third-party connectors using the SQL `Deploy` command. Refer [Deployment of Third Party Connectors](/connectors/deployment_dependency_jar.md) +You can also deploy other third party connectors using the SQL `Deploy` command. Refer [Deployment of Third Party Connectors](deployment_dependency_jar.md). You will likely find a Spark connector for your data source via the [Spark packages portal](https://spark-packages.org/) or doing a web search. -For more information see: - -* [START HERE - How to load data into SnappyData Tables](../howto/load_data_into_snappydata_tables.md) +* [START HERE - for a quick overview of the concepts and some examples for loading data](../howto/load_data_into_snappydata_tables.md) * [Data Loading examples using Spark SQL/Data Sources API](../howto/load_data_from_external_data_stores.md) * [Supported Data Formats](../Data/data_formats.md) -* [Accessing Cloud Storages](access_cloud_data.md) +* [Accessing Cloud Stores](access_cloud_data.md) * [Connecting to External Hive Metastores](../Data/external_hive_support.md) * [Using the SnappyData Change Data Capture (CDC) Connector](cdc_connector.md) * [Using the SnappyData GemFire Connector](gemfire_connector.md) + diff --git a/docs/experimental.md b/docs/experimental.md new file mode 100644 index 0000000000..da8ea2058f --- /dev/null +++ b/docs/experimental.md @@ -0,0 +1,16 @@ +# Experimental Features + +TIBCO ComputeDB 1.2.0 provides the following features on an experimental basis. These features are included only for testing purposes and are not yet supported officially: + +## Authorization for External Tables +You can enable authorization of external tables by setting the system property **CHECK_EXTERNAL_TABLE_AUTHZ** to true when the cluster's security is enabled. +System admin or the schema owner can grant or revoke the permissions on external tables to other users. +For example: `GRANT ALL ON to ;` + + +## Support ad-hoc, Interactive Execution of Scala code +You can execute Scala code using a new CLI script **snappy-scala** that is built with IJ APIs. You can also run it as an SQL command using prefix **exec scala**. +The Scala code can use any valid/supported Spark API for example, to carry out custom data loading/transformations or to launch a structured streaming job. Since the code is submitted as an SQL command, you can now also use any SQL tool (based on JDBC/ODBC), including Notebook environments, to execute ad-hoc code blocks directly. Prior to this feature, apps were required to use the smart connector or use the TIBCO ComputeDB specific native Zeppelin interpreter. +**exec scala** command can be secured using the SQL GRANT/REVOKE permissions. System admin (DB owner) can grant or revoke permissions for Scala interpreter privilege. + +For more information refer to [Executing Spark Scala Code using SQL](/programming_guide/scala_interpreter.md) diff --git a/docs/howto.md b/docs/howto.md index 663b37f17d..181caa136c 100644 --- a/docs/howto.md +++ b/docs/howto.md @@ -42,8 +42,6 @@ The following topics are covered in this section: * [How to Connect using JDBC Driver](howto/connect_using_jdbc_driver.md) -* [How to Store and Query JSON Objects](howto/store_and_query_json_objects.md) - * [How to Store and Query Objects](howto/store_and_query_objects.md) * [How to use Stream Processing with SnappyData](howto/use_stream_processing_with_snappydata.md) diff --git a/docs/howto/connect_oss_vis_client_tools.md b/docs/howto/connect_oss_vis_client_tools.md index d84b2a85ae..186f2b8889 100644 --- a/docs/howto/connect_oss_vis_client_tools.md +++ b/docs/howto/connect_oss_vis_client_tools.md @@ -40,6 +40,8 @@ To connect SnappyData from DbVisualizer, do the following: !!! Note The steps provided here are specific to DbVisualizer 10.0.20 version. The steps can slightly vary in other versions. +For secure connections, refer [Creating a Secure Connection from JDBC Client Tools](#secureconnectJDBC) + ## SQL Workbench/J @@ -70,6 +72,8 @@ To connect SnappyData from SQL Workbench/J, do the following: * Enter username and password. 8. Click the **Test** button and then click **OK**.
After you get a successful connection, you run queries in SnappyData from SQL WorkBench/J. +For secure connections, refer [Creating a Secure Connection from JDBC Client Tools](#secureconnectJDBC) + ## DBeaver DBeaver is a graphical database management tool. You can access SnappyData from DBeaver. Download and install DBeaver, start the LDAP server and print the LDAP conf, and then connect to SnappyData from DBeaver. @@ -82,16 +86,6 @@ To download and install DBeaver, do the following: 2. Choose an appropriate installer for the corresponding operating system. For example, for Linux Debian package, download from [this link](https://dbeaver.io/files/dbeaver-ce_latest_amd64.deb). 3. Run the corresponding commands that are specified in the **Install** section on the Download page. -### Starting the LDAP Server - -To start the LDAP server, do the following: - -1. From the terminal, go to the location of ldap-test-server:
`cd $SNAPPY_HOME/store/ldap-test-server` -2. Run the following command to build:
`./gradlew build` -3. Run the script:
`./start-ldap-server.sh auth.ldif`
- This starts the LDAP server and prints the LDAP conf. The printed LDAP conf contains the username and password of LDAP that should be used to connect from DBeaver. Copy this into all the conf files of SnappyData. -4. Start the SnappyData cluster. - ### Connecting to SnappyData from DBeaver 4. Launch DBeaver and click **New database connection**. 5. Select **Hadoop / Big Data** section from the left.
![Images](../Images/sql_clienttools_images/dbeaver_install1.png)
![Images](../Images/sql_clienttools_images/dbeaver_install2.png) @@ -101,6 +95,8 @@ To start the LDAP server, do the following: * Username / Password 7. Test the connection and finish the setup of the database source. +For secure connections, refer [Creating a Secure Connection from JDBC Client Tools](#secureconnectJDBC) + ## SQuirreL SQL Client @@ -116,18 +112,7 @@ To download and install SQuirrel, do the following: 3. Go to the SQuirreL SQL Client installation folder and run the following command:
`./squirrel-sql.sh` -### Starting the LDAP Server - -To start the LDAP server, do the following: - -1. From the terminal, go to the location of ldap-test-server:
`cd $SNAPPY_HOME/store/ldap-test-server` -2. Run the following command:
`./gradlew build` -3. Run the following script:
`./start-ldap-server.sh auth.ldif` - This starts the LDAP server and prints the LDAP conf. The printed LDAP conf contains username and password of LDAP that should be used to connect from SQuirreL SQL Client. Copy this into all the conf files of SnappyData. -4. Start SnappyData cluster. - - -### Connecting to SnappyData from SQuirreL SQL Client +### Connecting to SnappyData from SQuirreL SQL Client To connect SnappyData from SQuirreL SQL Client, do the following: @@ -166,8 +151,21 @@ jdbc jar: https://mvnrepository.com/artifact/io.snappydata/snappydata-jdbc_2.11/ drop table if exists colTable; show tables; +For secure connections, refer [Creating a Secure Connection from JDBC Client Tools](#secureconnectJDBC) !!!Note When connecting to SnappyData, if a SQL client tool sets JDBC autocommit to false and transaction isolation level such as read committed or repeatable read is used, the unsupported operations such as those on column table will produce an error - **Operations on column tables are not supported when query routing is disabled or autocommit is false.** In such cases, connection property **allow-explicit-commit=true** can be used in the connection URL to avoid this error. Refer to configuration parameters section for details on this property. For example, JDBC URL: **jdbc:snappydata://locatoHostName:1527/allow-explicit-commit=true** + +## Creating a Secure Connection from JDBC Client Tools + +If you already have an LDAP server, you can use the same to connect to SnappyData cluster or you can use the LDAP server that comes pre-configured with SnappyData. +To start the pre-configured LDAP server of SnappyData, do the following: + +1. From the terminal, go to the location of ldap-test-server:
`cd $SNAPPY_HOME/store/ldap-test-server` +2. Run the following command to build:
`./gradlew build` +3. Run the script:
`./start-ldap-server.sh auth.ldif`
+ This starts the LDAP server and prints the LDAP conf. The printed LDAP conf contains the username and password of LDAP that should be used to connect from JDBC clients. Copy this into the leads/servers/locators conf files of SnappyData. +4. Start the SnappyData cluster. +5. When you are connecting from a JDBC client, ensure to provide the user name and password printed in step 3. diff --git a/docs/howto/load_data_from_external_data_stores.md b/docs/howto/load_data_from_external_data_stores.md index 36c15b501b..357f702d06 100644 --- a/docs/howto/load_data_from_external_data_stores.md +++ b/docs/howto/load_data_from_external_data_stores.md @@ -1,7 +1,7 @@ # How to Load Data from External Data Stores (e.g. HDFS, Cassandra, Hive, etc) -SnappyData comes bundled with the libraries to access HDFS (Apache compatible). You can load your data using SQL or DataFrame API. +SnappyData comes bundled with the libraries to access HDFS (Apache compatible). You can load your data using SQL or DataFrame API. ## Example - Loading data from CSV file using SQL @@ -76,7 +76,7 @@ df.write.format("column").saveAsTable("columnTable") ## Importing Data using JDBC from a relational DB !!! Note - Before you begin, you must install the corresponding JDBC driver. To do so, copy the JDBC driver jar file in **/jars** directory located in the home directory and then restart the cluster. + Before you begin, you must install the corresponding JDBC driver. Refer to [Deploying Third Party Connectors](/connectors/deployment_dependency_jar.md). @@ -135,7 +135,7 @@ Refer to the [Spark SQL JDBC source access for how to parallelize access when de The example below demonstrates how you can load data from a NoSQL store: !!! Note - Before you begin, you must install the corresponding Spark-Cassandra connector jar. To do so, copy the Spark-Cassandra connector jar file to the **/jars** directory located in the home directory and then restart the cluster. + Before you begin, you must install the corresponding Spark-Cassandra connector jar. Refer to [Deploying Third Party Connectors](/connectors/deployment_dependency_jar.md). diff --git a/docs/howto/use_synopsis_data_engine_to_run_approximate_queries.md b/docs/howto/use_synopsis_data_engine_to_run_approximate_queries.md index 15cc2d5076..2c83713ade 100644 --- a/docs/howto/use_synopsis_data_engine_to_run_approximate_queries.md +++ b/docs/howto/use_synopsis_data_engine_to_run_approximate_queries.md @@ -1,11 +1,11 @@ -# How to use Synopsis Data Engine to Run Approximate Queries +# How to use Approximate Query Processing to Run Approximate Queries -Synopsis Data Engine (SDE) uses statistical sampling techniques and probabilistic data structures to answer analytic queries with sub-second latency. There is no need to store or process the entire dataset. The approach trades off query accuracy for fast response time. -For more information on SDE, refer to [SDE documentation](../aqp.md). +Approximate Query Processing (AQP) uses statistical sampling techniques and probabilistic data structures to answer analytic queries with sub-second latency. There is no need to store or process the entire dataset. The approach trades off query accuracy for fast response time. +For more information on AQP, refer to [AQP documentation](../aqp.md). **Code Example**: -The complete code example for SDE is in [SynopsisDataExample.scala](https://github.com/SnappyDataInc/snappydata/blob/master/examples/src/main/scala/org/apache/spark/examples/snappydata/SynopsisDataExample.scala). The code below creates a sample table and executes queries that run on the sample table. +The complete code example for AQP is in [SynopsisDataExample.scala](https://github.com/SnappyDataInc/snappydata/blob/master/examples/src/main/scala/org/apache/spark/examples/snappydata/SynopsisDataExample.scala). The code below creates a sample table and executes queries that run on the sample table. **Get a SnappySession**: ```pre @@ -36,7 +36,7 @@ snSession.sql("CREATE TABLE AIRLINE USING column AS (SELECT Year AS Year_, " + ``` **Create a sample table for the above base table**: -Attribute 'qcs' in the statement below specifies the columns used for stratification and attribute 'fraction' specifies how big the sample needs to be (3% of the base table AIRLINE in this case). For more information on Synopsis Data Engine, refer to the [SDE documentation](../aqp.md#working-with-stratified-samples). +Attribute 'qcs' in the statement below specifies the columns used for stratification and attribute 'fraction' specifies how big the sample needs to be (3% of the base table AIRLINE in this case). For more information on Approximate Query Processing, refer to the [AQP documentation](../aqp.md#working-with-stratified-samples). ```pre diff --git a/docs/impt_info/Important_Information_CE.pdf b/docs/impt_info/Important_Information_CE.pdf index dd6838810ceff8353a592c266abb334661908c7c..74734ae83d580c9fc9b3634764e2d0eed517bc5e 100644 GIT binary patch literal 103922 zcmdSAWmsIxwl+#|cZWdZ5VUb8xVyVH4vo9Jg+Opgun-6uAV_d0cyJ9)aCg5Q)?R0? z_3XXR`M&3#`{U9yJ!j7vHAdAK^;Xq83K|s&DP|xG4;nH}E#wCc83q>k!PL=;9^}ZZsm=gkRd;i7H+R!;0fE%OU^f6e z$8!-yQx`i~M@ulIn%|co3jm}TNLZLv5#(lS0jU$f^IMe~tCkJO6Xc@e0*s zI(VLy2n(YjgB&fMZTbDh-;8=ThE*Kw?g&xHDsN-qstgWcs))m0{N9`_PcPEIxXAepLzF}2o)o?L& zbandODsyjEadlP+kcW*qNKIM{Qm=-onJc8oAHMpd<*op>zjo5^ivR2+DI0q?kPEAn zJ)~6L)l!fOl&lXwnRKqdUIBHaE;86Co| zWf*0sq~6Qv$XB_MItV`vus7G@JuW}KfNiLxZr`H?c$5`aJyPZvU505R^>MqK= z)_w1L;OIMK{>J8r{Wx}j=NGqZp2lXz-_2S8H;6JVa^}-zC+rk4Lwm0^yY6|dPNw7p zN97bA!a62Gavs4D*MFkgI*CozEQ?HPzB*-BfrX+6j65>0d;My~$ET+|PD$e(d%iJS 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zX}A;2(aOQu3Skep!iAb5T%1Md=&lI;@%in!>>U0eWbefJ{TVr72s@w~)By8J&akTyZP-ZY7+!k(k6_-X*1pl&~!%y<~3!BISWFzi>)(EM# z-=z5WR&GC0L2h0_Fc%w$SCAVd z4E$ToKjr!zLkhWdCUS4gUt@rO#`vk`-!OhRj9w@Xw+@30&28wsN+G|BdJsCla=QdxSrky&5b8ZK3uSB6MyXW^i+;i>)&q@NXF3 z8UHKDcP9Q$?}`VrKL?J#q5LN~`8CxmeqdKU3E}t)UjC{Hq)7ghil~y6hdV$Hj<9h5 zcXatna>7WJ3933G%v@k_N6{YxJ+k`%l&nl0p^omz9>j?p4*pWHQ<0CT>M~e5D%}wci4ZZ^;dK^K`Dft1JvIAAJLHo zKZexbh4(`iKfM~h7yP)3eDNUPdB8tjdcU{%A=m%Z@y}=ZPf%Zdq zwl|7YUD`O|HN+arBtSEN(?3%4A673lltU+(`c2+RAg zgRs2FAZ&TXi0dHmEt?u>;$%>XC4!F^3kQ>h-0z+SAw?0bx^7eew^1J!lz3*y`>y}${y`Ea#YzHR3%g$QuwlD z?c};+Zx&@ftR9s~=hj*_AG0xql@x zL79dtGW|+AKdqROhQknMaO5&8yWbXWx!Bn|Ap@$aj>xrNFyzD=0Oq<1HCx#_!ySRL zwpU*5_a?x5()z-ZT-?$!5|Z2k60%@^Fc0#SA1ng`NeXcDA%h;$lA^+3Sui)h1W1;T zk6%_wf=31d;uDbMmzI%{mIDtn*-0Wbhvx hi2L{#vP1f*j?Qk5aC1ByFqjVy=k8q@6 ## Prerequisites @@ -189,7 +191,7 @@ bin/snappy-job.sh submit ## Stopping the SnappyData Cluster on Kubernetes -To stop the SnappyData cluster on Kubernetes, you must delete the **SnappyData Helm **chart using the `helm delete` command. +To stop the SnappyData cluster on Kubernetes, you must delete the **SnappyData Helm** chart using the `helm delete` command. ``` $ helm delete --purge snappydata @@ -241,7 +243,7 @@ servers: You can specify SnappyData [configuration parameters](https://snappydatainc.github.io/snappydata/configuring_cluster/configuring_cluster/#configuration-files) in the **servers.conf**, **locators.conf**, and **leaders.conf** attributes for servers, locators, and leaders respectively. -## Kubernetes Obects Used in SnappyData Chart +## Kubernetes Objects Used in SnappyData Chart This section provides details about the following Kubernetes objects that are used in SnappyData Chart: @@ -363,3 +365,17 @@ When SnappyData chart is installed, the **log4.properties** file will be used t + +## Mounting ConfigMaps +Files created in **charts/snappydata/conf/** are mounted on SnappyData server, lead, and locator pods as configmaps and copied into the product's **conf** directory. This is useful to make configuration files (such as spark-env.sh, +metrics.properties etc.) available to the product. Template files are provided in the +**charts/snappydata/conf/**. These template files can be renamed and edited to provide configuration. + +For example: + +``` +$ cd snappydata/conf/ +$ cp spark-env.sh.template spark-env.sh +# now modify the spark-env.sh to specify configuration +``` + diff --git a/docs/monitoring/monitoring.md b/docs/monitoring/monitoring.md index fc28a8e288..9b1a4ab941 100644 --- a/docs/monitoring/monitoring.md +++ b/docs/monitoring/monitoring.md @@ -21,6 +21,8 @@ The following topics are covered in this section: * [Jobs](#jobs) +* [Structured Streaming](#structuredstream) + * [Stages](#stages) * [Spark Cache](#spark_cache) @@ -268,7 +270,7 @@ On this page, you can view the total time required for all the tasks in a job to * The **Structured Streaming** tab is available in the Embedded mode as well as in the Smart Connector mode. However, in the Smart Connector mode, this tab is visible only if you use the Snappy-Spark distribution. In case you use the upstream or stock spark distribution, then the **Structured Streaming** tab will not be visible. * Only those streaming queries that are started using SnappySession will be available for monitoring on the UI. Queries that are started using SparkSession won't be available for monitoring on the UI. -You can view the **Structured Streaming** tab only for the monitoring console of the corresponding cluster. For example, if you submit a streaming query on the embedded cluster using snappy-job, then the **Structured Streaming** tab is available on the monitoring console of the embedded cluster. If you submit a streaming query as a Smart Connector job, on a separate spark cluster, then the **Structured Streaming** tab is available on the monitoring console of the Spark cluster where the Smart Connector job is submitted. +You can view the **Structured Streaming** tab only for the monitoring console of the corresponding cluster. For example, if you submit a streaming query on the embedded cluster using [snappy-job](/programming_guide/snappydata_jobs.md), then the **Structured Streaming** tab is available on the monitoring console of the embedded cluster. If you submit a streaming query as a Smart Connector job, on a separate spark cluster, then the **Structured Streaming** tab is available on the monitoring console of the Spark cluster where the Smart Connector job is submitted. The following details are shown on the **Structured Streaming** tab: @@ -283,7 +285,7 @@ The following details are shown on the **Structured Streaming** tab: |**Uptime**| Total time that has passed since the streaming has started.| |**Trigger** **Interval**| Time interval on which successive batch is expected to start it’s execution. Each event is fired on this interval but each event may or may not have batches to process.| |**Batches Processed**| Total number of batches processed since streaming query has been started.| -|**Status**| Status can be **Active** or **Inactive**.
* **Active**: Query is running
* **Inactive**: Query has stopped or failed| +|**Status**| Status can be **Active** or **Inactive**.
* **Active**: Query is running.
* **Inactive**: Query has stopped or failed.| |**Total Input Records**| Total count of records received from the sources and processed since the streaming query has been started.| |**Current Input Rate**| Number of input records per second in a batch, which has been received in the current event trigger. This value is the number of records received between the start time of previous event and start time of current event and divided by the number of seconds between these two events.| |**Current Processing Rate**|Average input records processed per second. This value is number of input records processed per second between the start time and the end time of the current event.| diff --git a/docs/prev_doc_ver.md b/docs/prev_doc_ver.md index cd79387f26..410c4d4aa2 100644 --- a/docs/prev_doc_ver.md +++ b/docs/prev_doc_ver.md @@ -3,6 +3,7 @@ Click a release to check the corresponding archived product documentation of SnappyData: +* [SnappyData 1.1.1](https://snappydata-docs.readthedocs.io/en/community_docv1.1.1/) * [SnappyData 1.1.0](https://snappydata-docs.readthedocs.io/en/community_docv1.1.0/) @@ -14,4 +15,6 @@ Click a release to check the corresponding archived product documentation of Sna * [SnappyData 1.0.0](https://snappydata-docs.readthedocs.io/en/docv1.0.0/) +* [SnappyData 1.1.1](https://snappydata-docs.readthedocs.io/en/community_docv1.1.1/) + diff --git a/docs/programming_guide/scala_interpreter.md b/docs/programming_guide/scala_interpreter.md index 575a1a1d49..7db50557f7 100644 --- a/docs/programming_guide/scala_interpreter.md +++ b/docs/programming_guide/scala_interpreter.md @@ -1,45 +1,80 @@ -# Using Scala Interpreter +# Executing Spark Scala Code using SQL -Users familiar with the command-line interpreter tools like Scala can experiment, play around with Scala programming language easily, and in frequent iterations. It also facilitates interactive programming. -Usually, any small change required would not only mean modifying the source code but also recompile, re-deploy, or re-run it. Interpreters do away with such complexities as they accept a program as input and readily display results as output. -Apache Spark has a command-line utility called **spark-shell**, which is primarily built on the Scala interpreter. Through this utility, you can launch spark-shell with any of the cluster managers. It can be launched in a local mode as well as in a client mode. - - -If you want to use a spark-shell to connect to a SnappyData cluster, you can do so only in the Smart Connector mode. However, if you want to execute any code in the store cluster (embedded mode), you have to comply with the contract that is defined per Spark Job Server, compile, and package the code in a JAR and then deploy the code using the Snappy-job command. This is cumbersome, especially for developers who want to iterate over the code quickly. Moreover, debugging such jobs is tricky as you must execute the job asynchronously, and any error requires you to track the JobIDs and poll the system to know the state of the job. +!!!Note + This is an experimental feature in the SnappyData 1.2.0 release. -SnappyData provides the following, on an experimental basis, to facilitate the execution of Scala code directly in an Embedded cluster mode. +Prior to the 1.2.0 release, any execution of a Spark scala program required the user to compile his Spark program, comply to specific callback API required by SnappyData, package the classes into a JAR, and then submit the application using **snappy-job **tool. +While, this procedure may still be the right option for a production application, it is quite cumbersome for the developer or data scientist wanting to quickly run some Spark code within the SnappyData store cluster and iterate. +With the introduction of the [**exec scala** SQL command](/reference/sql_reference/exec-scala.md), you can now get a JDBC or ODBC connection to the cluster and submit ad-hoc scala code for execution. The JDBC connection provides an ongoing session with the cluster so applications can maintain state and use/return this across multiple invocations. -* **exec scala SQL**
- This is a SQL construct from any JDBC/ODBC client and one of the ways in which you can submit the Scala code to the SnappyData cluster. +Beyond this developer productivity appeal, this feature also allows you to skip using the [**Smart Connector**](/affinity_modes/connector_mode.md) in several cases. +Bespoke Spark applications using the smart connector are required to launch a client Spark application with its own executors and the store cluster is mostly providing parallel access to managed data. As the client is running queries this is additional capacity that you must budget. Moreover, the overall architecture is tough to understand. With **exec scala**, any Spark application could submit scala code as an SQL command now. -* **Scala interpreter CLI utility**
- You can also submit Scala code to the SnappyData cluster using this command-line utility, which is similar to a spark-shell. +**Here is one use case that motivated us**: TIBCO data scientists using Team Studio (TIBCO Data Science platform) can now build custom operators, target SnappyData for in-database computations, and run adhoc Spark Scala code. For instance, ETL feature engineering using Spark ML or running a training job in-memory and with parallelism. +Following are some examples to demonstrate the usage through some examples .... -## exec scala +### Example 1: +``` +// Note this is a SQL command... this is the text you would send using a JDBC or ODBC connection -**exec scala** is an SQL feature that you can use to submit Scala code to the SnappyData cluster. You can submit a chunk of Scala code similar to a SQL query, which is submitted to a database on a JDBC/ODBC connection including hive thrift server SQL clients such as beeline. +val prepDataCommand = + """ Exec scala + val dataDF = snapp.read.option("header", "true").csv("../path/to/customers.csv") + // Variable 'snapp' is injected to your program automatically. Refers to a SnappySession instance. + val newDF = dataDF.withColumn("promotion", "$20") + newDF.createOrReplaceTempView("customers") + //OR, store in in-memory table + newDF.write.format("column").saveAsTable("customers") +""" +// Acquire JDBC connection and execute Spark program +Class.forName("io.snappydata.jdbc.ClientDriver") +val conn = DriverManager.getConnection("jdbc:snappydata://localhost:1527/") +conn.createStatement().executeSQL(prepDataCommand) +``` -A parallel between a SQL query and a block of Scala code is brought about by using a fixed schema for the Scala code. Since, a select query's result set metadata is fixed, whereas there is no meaning of a fixed schema for a chunk of Scala code. Therefore, SnappyData provides a fixed schema to all the Scala code. This is elaborated in the following sections. +### Example 2: +``` + // Return back some Dataframe ... you use keyword returnDF to indicate the variable name in scala to return + +val getDataFromCDB = + """ exec scala options(returnDF 'someDF') + val someDF = snapp.table("customers").filter(..... ) + """ +ResultSet rs = conn.createStatement().executeSQL(prepDataCommand) +//Use JDBC ResultSet API to fetch result. Data types will be mapped automatically +``` ### Syntax ``` -exec scala [options (returnDF ‘dfName’)] k +exec scala [options (returnDF ‘dfName’)] ``k ``` * **exec** and **scala** are the keywords to identify this SQL type. + * **options** is an optional part of the syntax. If it is present, then after the keyword **options**, you can specify the allowed options inside parentheses. Currently, only one optional parameter, that is **returnDF**, can be specified with the execution. For this option, you can provide the name of any actual symbol in the Scala code, which is of type DataFrame. Through the **returnDF** option, you can request the system to return the result of the specific dataframe, which got created as the result of the Scala code execution. By default, the **exec scala** just returns the output of each interpreted line, which the interpreter prints on the Console after executing each line. -For more details, refer to the Examples section. + +## How does it work? + +All the code from **exec scala** is executed using the Scala REPL on the SnappyData Lead node. When Spark Dataframes are invoked this would automatically result in workload distribution across all the SnappyData +servers. The Lead node manages a pool of REPL based interpreters. The user SQL activity is delegated to one of the interpreters from this pool. The pool is lazily created. + +Any connection (JDBC or ODBC) results in the creation of a SnappySession within the CDB cluster. Moreover, the session remains associated with the connection until it is closed or dereferenced. + + +The first time **exec scala** is executed, an interpreter from the pool gets associated with the connection. This allows the user to manage any adhoc private state on the server side. For example, any variables, objects, or even classes created will be isolated from other users. +The functioning of the interpreter is same as that of the interactive [Spark-shell](/programming_guide/using_the_spark_shell_and_spark-submit.md) only with one difference. As commands are interpreted any output generated will be cached in a buffer. And, when the command is done, the cached output will be available in the client side ResultSet object. + ### Securing the Usage of exec scala SQL -The ability to run Scala code directly into a running cluster can affect the health of the cluster. This is because there is no check on the type of code that you want to execute in the cluster. The submitted Scala code is taken to the lead node and is executed there. An erroneous code can bring down the lead node and thereby make the SnappyData cluster unavailable. Hence, it becomes essential to secure the use of this functionality. +The ability to run Scala code directly on a running cluster can be dangerous. This is because there are no checks on what code you can run. The submitted Scala code is executed on the lead node and has the potential to bring it down. It becomes essential to secure the use of this functionality. By default, in a secure cluster, only the database owner is allowed to run Scala code through **exec scala** SQL or even through snappy-scala shell. The database owner is the user who brings up the SnappyData cluster. If different credentials are used for different components of the SnappyData cluster, then the credentials with which the lead node is started becomes the database owner for this purpose. Ideally, every node should start with the same superuser credentials. @@ -63,7 +98,7 @@ grant privilege exec scala to LDAPGROUP:group1 revoke privilege exec scala from user2,LDAPGROUP:group1 ``` -### Examples +### More Examples The **snappy** CLI, which is also known as the snappy-sql CLI, is commonly used to fire SQL queries interactively into a SnappyData cluster. You can use these command-line tools to fire Scala code as well. @@ -93,11 +128,11 @@ x: Int = 5 ``` ## The following Scala code creates a table using the available snappy session. This can be accessed through the symbol ‘snappy’. It then inserts a couple of rows, obtains a dataframe object, and then uses the df.show command. -snappy> exec scala snappy.sql("create table t1(c1 int not null)") +snappy> exec scala snappysession.sql("create table t1(c1 int not null)") /* This is a data frame test */ // Check the collect output - snappy.sql("insert into t1 values (1), (2)") - val df = snappy.table("t1") + snappysession.sql("insert into t1 values (1), (2)") + val df = snappysession.table("t1") val cnt = df.count df.show; C0 @@ -117,7 +152,7 @@ cnt: Long = 2 ``` !!!Note - The variable **session** is not declared anywhere; however, the above code uses it. This **snappy** symbol name is for the object **SnappySession**, which represents this database connection. Similarly, **sc** is there to access the singleton SparkContext present on the lead node. + The variable **snappysession** is not declared anywhere; however, the above code uses it. The **snappysession** symbol name is for the object **SnappySession**, which represents this database connection. Similarly, **sc** is there to access the singleton SparkContext present on the lead node. **Example 3** @@ -144,19 +179,6 @@ c !!!Note The use of the **sc** symbol here in this example. This is the global SparkContext present in the lead node. -### Important Information About **exec scala** - -- Arbitrary Scala code can be executed through exec scala. -- A SnappySession object and the SparkContext object can be accessed through the Symbol name **snapp** and **sc** in the **exec scala** code. -- If you are having a loop inside your Scala code, then the output of each loop is not returned after the execution of each loop. The entire execution happens first, and after that, the output is fetched at once. This is unlike the spark-shell. In a spark-shell or Scala shell, the output of each loop execution is printed on the console. -- If you run rogue code such as **System.exit** or a divide by 0 scenario, the lead node is bound to crash. The system does not check the code before executing it. -- In a non-secured system, anyone can run **exec scala**. -- In a secured system only the dbowner or database owner can run **exec scala** by default. -- However, a database owner can grant and revoke privileges to and from users and LDAP groups. -- Multiple sessions can run **exec scala**. These sessions can be concurrent too. All the sessions are isolated from each other, that is any variables or/and types defined in one session that is visible only in the same session. -- By default, the output of this SQL is a single column resultset of type **varchar**. Essentially, whatever the interpreter writes on the console is brought back to the caller/user. -- The user can specify the name of the dataframe instead as a return result instead of the default behavior. The symbol should be of a valid existing dataframe symbol in the context of the session. - ### Known Issues and Limitations An interpreter cannot serialize a dependent closure properly. A dependent closure is the closure that refers to some other class, function, or variable that is defined outside the closure. @@ -165,7 +187,7 @@ Thus, the following example fails with closure serialization error. ``` exec scala def multiply(number: Int, factor: Int): Int = { - number \* factor + number * factor } @@ -173,7 +195,7 @@ val data = Array(1, 2, 3, 4, 5) val numbersRdd = sc.parallelize(data, 1) -val collectedNumbers = numbersRdd.map(multiply(\_, 2).toString()).collect() +val collectedNumbers = numbersRdd.map(multiply(_, 2)).collect() ``` The execution of the last line fails as the closure cannot be serialized due to this issue. This is referring to the function **multiply** that is defined outside the closure. @@ -182,51 +204,14 @@ Similarly, even the following example fails: ``` -Val x = 5 +val x = 5 val data = Array(1, 2, 3, 4, 5) val numbersRdd = sc.parallelize(data, 1) -val collectedNumbers = numbersRdd.map(\_ \* x).toString()).collect() +val collectedNumbers = numbersRdd.map(_ * x).collect() ``` This is because the closure is referring to **x**, which is defined outside the closure. There are no issues if the closure has no dependency on any external variables. - -## snappy-scala CLI - -The snappy-scala CLI, which is similar to spark-shell in its capabilities, is introduced as an experimental feature in this release. snappy-scala CLI is built on top of the **exec scala** feature and the already existing snappy CLI utility. This is not a true Scala interpreter but mimics a scala or a spark-shell kind of interpreter. Here, each line of code is shipped to the lead node. The code is interpreted on the lead node. The result is brought back to the snappy-scala shell. - -Features such as auto-complete and full-fledged colon command of a scala shell are not as stable as in the spark-shell. This is because in Apache Spark, the spark-shell is itself the Application driver VM whereas in SnappyData, the driver is the lead node, which is a remote process from the command-line utility point of view. The snappy-scala CLI utility connects like a client utility and therefore has the limitation mentioned above. Those limitations can be overcome and will be considered in a future release. - -### Command-line options - -``` -$ ./bin/snappy-scala -h - -Usage: - -snappy-scala [OPTIONS] - -OPTIONS and Default values - - -c LOCATOR_OR_SERVER_ENDPOINT (default value is localhost:1527) - - -u USERNAME (default value is APP) - - -p PASSWORD (default value is APP) - - -r SCALA_FILE_PATHS (comma separated paths if multiple) - - -h, --help (prints script usage) - -``` - - -As seen above there are 5 - -### Securing the Usage of snappy-scala CLI - -This is the same as securing the usage of **exec scala**. For more details refer to [Securing the Usage of exec scala SQL](#secureexscala). - diff --git a/docs/quickstart/getting_started_by_installing_snappydata_on-premise.md b/docs/quickstart/getting_started_by_installing_snappydata_on-premise.md index fbd7036207..0aa0e20b55 100644 --- a/docs/quickstart/getting_started_by_installing_snappydata_on-premise.md +++ b/docs/quickstart/getting_started_by_installing_snappydata_on-premise.md @@ -10,6 +10,6 @@ $ mkdir quickstartdatadir $./bin/spark-shell --conf spark.snappydata.store.sys-disk-dir=quickstartdatadir --conf spark.snappydata.store.log-file=quickstartdatadir/quickstart.log ``` -It opens the Spark shell. All SnappyData metadata, as well as persistent data, is stored in the directory **quickstartdatadir**.
The spark-shell can now be used to work with SnappyData using [SQL](using_sql.md) and [Scala APIs](using_spark_scala_apis.md). + It opens the Spark shell. All TIBCO ComputeDB metadata, as well as persistent data, is stored in the directory **quickstartdatadir**.
The spark-shell can now be used to work with TIBCO ComputeDB using [SQL](using_sql.md) and [Scala APIs](using_spark_scala_apis.md). -Follow instructions [here](/howto/use_apache_zeppelin_with_snappydata.md), to use the product from Apache Zeppelin. \ No newline at end of file +Follow instructions [here](/howto/use_apache_zeppelin_with_snappydata.md), to use the product from Apache Zeppelin. diff --git a/docs/reference/API_Reference/apireference_guide.md b/docs/reference/API_Reference/apireference_guide.md index aaaed3f6aa..bbd5651f09 100644 --- a/docs/reference/API_Reference/apireference_guide.md +++ b/docs/reference/API_Reference/apireference_guide.md @@ -226,7 +226,8 @@ snappySession.dropTable(“t1”, true) Creates a stratified sample table. -!!! Note This API is not supported in the Smart Connector mode. +!!! Note + This API is not supported in the Smart Connector mode. **Syntax** @@ -261,7 +262,8 @@ snappySession.createSampleTable("airline_sample", Some("airline"), Map("qcs" - Creates an approximate structure to query top-K with time series support. -!!! Note This API is not supported in the Smart Connector mode. +!!! Note + This API is not supported in the Smart Connector mode. **Syntax** @@ -276,9 +278,9 @@ createApproxTSTopK(topKName: String, baseTable: Option[String], keyColumnName: |--------|--------| | topKName | The qualified name of the top-K structure. | |baseTable |The base table of the top-K structure, if any.| -| keyColumnName || -|inputDataSchema| | -|topkOptions| | +| keyColumnName | Top-k key column for aggregation| +|inputDataSchema|Schema of input data | +|topkOptions| Extra options including the following:
**frequencyCol**: column to use for top-k frequency count.
**epoch, timeInterval**: start and interval for collecting samples.
**timeSeriesColumn**: A column that accurately records timestamps for better handling of time range queries| |allowExisting |When set to **true**, tables with the same name are ignored, else a **table exist** exception is shown.| |Returns |Dataframe | @@ -297,7 +299,7 @@ Sets the current database/schema. **Syntax** ``` -schema +setCurrentSchema(schema: String) ``` @@ -305,13 +307,13 @@ schema |Parameter | Description | |--------|--------| -| schemaName| schema name which goes into the catalog. | +| schema| schema name which goes into the catalog. | |Returns|Unit| **Example ** ```pre -snappySession.setSchema(“APP”) + snappySession.setCurrentSchema("APP") ``` @@ -449,7 +451,8 @@ snappySession.delete(“t1”, s"col1=$i")) Fetches the topK entries in the** Approx TopK** synopsis for the specified time interval. The time interval specified here should not be less than the minimum time interval used when creating the TopK synopsis. -!!! Note This API is not supported in the Smart Connector mode. +!!! Note + This API is not supported in the Smart Connector mode. **Syntax** diff --git a/docs/reference/command_line_utilities/scala-cli.md b/docs/reference/command_line_utilities/scala-cli.md new file mode 100644 index 0000000000..f5788f4884 --- /dev/null +++ b/docs/reference/command_line_utilities/scala-cli.md @@ -0,0 +1,26 @@ +# snappy-scala CLI + +The snappy-scala CLI, which is similar to spark-shell in its capabilities, is introduced as an experimental feature in this release. snappy-scala CLI is built on top of the **exec scala** feature and the already existing snappy CLI utility. This is not a true Scala interpreter but mimics a scala or a spark-shell kind of interpreter. Here, each line of code is shipped to the lead node. The code is interpreted on the lead node. The result is brought back to the snappy-scala shell. + +Features such as auto-complete and full-fledged colon command of a scala shell are not as stable as in the spark-shell. This is because in Apache Spark, the spark-shell is itself the Application driver VM whereas in SnappyData, the driver is the lead node, which is a remote process from the command-line utility point of view. The snappy-scala CLI utility connects like a client utility and therefore has the limitation mentioned above. Those limitations can be overcome and will be considered in a future release. + +### Command-line options + +``` +$ ./bin/snappy-scala -h +Usage: +snappy-scala [OPTIONS] +OPTIONS and Default values + -c LOCATOR_OR_SERVER_ENDPOINT (default value is localhost:1527) + -u USERNAME (default value is APP) + -p PASSWORD (default value is APP) + -r SCALA_FILE_PATHS (comma separated paths if multiple) + -h, --help (prints script usage) +``` + + +As seen above there are 5 + +### Securing the Usage of snappy-scala CLI + +This is the same as securing the usage of **exec scala**. For more details refer to [Securing the Usage of exec scala SQL](/programming_guide/scala_interpreter.md#secureexscala). diff --git a/docs/reference/sql_reference/exec-scala.md b/docs/reference/sql_reference/exec-scala.md new file mode 100644 index 0000000000..0554f544eb --- /dev/null +++ b/docs/reference/sql_reference/exec-scala.md @@ -0,0 +1,50 @@ +# exec scala + +## Syntax + + exec scala [options (returnDF ‘dfName’)] + +* **exec** and **scala** are the keywords to identify this SQL type. + +* **options** is an optional part of the syntax. If it is present, then after the keyword **options**, you can specify the allowed options inside parentheses. Currently, only one optional parameter, that is **returnDF**, can be specified with the execution. For this option, you can provide the name of any actual symbol in the Scala code, which is of type DataFrame. +Through the **returnDF** option, you can request the system to return the result of the specific dataframe, which got created as the result of the Scala code execution. By default, the **exec scala** just returns the output of each interpreted line, which the interpreter prints on the Console after executing each line. + +## Description + +**exec scala** is an SQL feature that you can use to submit Scala code to the SnappyData cluster. This is a SQL construct from any JDBC/ODBC client and one of the ways in which you can submit the Scala code to the SnappyData cluster. You can submit a chunk of Scala code similar to a SQL query, which is submitted to a database on a JDBC/ODBC connection including hive thrift server SQL clients such as beeline. + +A parallel between a SQL query and a block of Scala code is brought about by using a fixed schema for the Scala code. Since, a select query's result set metadata is fixed, whereas there is no meaning of a fixed schema for a chunk of Scala code. Therefore, SnappyData provides a fixed schema to all the Scala code. This is elaborated in the following sections. + +### Important Information About **exec scala** + +- Arbitrary Scala code can be executed through exec scala. +- A SnappySession object and the SparkContext object can be accessed through the Symbol name **snapp** and **sc** in the **exec scala** code. +- If you are having a loop inside your Scala code, then the output of each loop is not returned after the execution of each loop. The entire execution happens first, and after that, the output is fetched at once. This is unlike the spark-shell. In a spark-shell or Scala shell, the output of each loop execution is printed on the console. +- If you run rogue code such as **System.exit** or a divide by 0 scenario, the lead node is bound to crash. The system does not check the code before executing it. +- In a non-secured system, anyone can run **exec scala**. +- In a secured system only the dbowner or database owner can run **exec scala** by default. +- However, a database owner can grant and revoke privileges to and from users and LDAP groups. +- Multiple sessions can run **exec scala**. These sessions can be concurrent too. All the sessions are isolated from each other, that is any variables or/and types defined in one session that is visible only in the same session. +- By default, the output of this SQL is a single column resultset of type **varchar**. Essentially, whatever the interpreter writes on the console is brought back to the caller/user. +- The user can specify the name of the dataframe instead as a return result instead of the default behavior. The symbol should be of a valid existing dataframe symbol in the context of the session. + +## Example + +A simple Scala code to define a value x and print it. + +``` +snappy> exec scala val x = 5 + /* This is a test + program */ + // Just see the value of x + println(x); +C0 +-------------------------------------------------------------------------------------------------------------------------------- +x: Int = 5 + +5 +``` + + + + diff --git a/docs/reference/sql_reference/grant-all.md b/docs/reference/sql_reference/grant-all.md new file mode 100644 index 0000000000..d6aab941ce --- /dev/null +++ b/docs/reference/sql_reference/grant-all.md @@ -0,0 +1,21 @@ +# GRANT ALL + +## Syntax + + +```pre +GRANT ALL ON to ; +``` + +## Description + +This syntax is used to grant the permissions on external tables for other users. + + +## Example + +Here is an example SQL to grant privileges to individual users on external tables: + +```pre +GRANT ALL ON EXT_T1 TO samb,bob; +``` diff --git a/docs/reference/sql_reference/pivot.md b/docs/reference/sql_reference/pivot.md index ed808c9e61..d36bf0b88e 100644 --- a/docs/reference/sql_reference/pivot.md +++ b/docs/reference/sql_reference/pivot.md @@ -1,9 +1,8 @@ # PIVOT -## Syntax ## Description -TIBCO ComputeDB parser support for PIVOT clause. It deviates from Spark 2.4 support in that it +SnappyData parser support for PIVOT clause. It deviates from Spark 2.4 support in that it only allows literals in the value IN list rather than named expressions. On the contrary, it supports explicit GROUP BY columns with PIVOT instead of always doing implicit detection (which may be different from what user needs in some cases). diff --git a/docs/reference/sql_reference/revoke_all.md b/docs/reference/sql_reference/revoke_all.md new file mode 100644 index 0000000000..0589524022 --- /dev/null +++ b/docs/reference/sql_reference/revoke_all.md @@ -0,0 +1,20 @@ +# REVOKE ALL + +## Syntax + +```pre +REVOKE ALL ON FROM ; +``` + + +## Description + +The REVOKE ALL statement removes permissions from a specific user on external Tables. + +## Examples + +Here is an example SQL to revoke permissions to individual users on external tables: + +```pre +REVOKE ALL ON EXT_T1 FROM samb,bob; +``` diff --git a/docs/reference/sql_reference/select.md b/docs/reference/sql_reference/select.md index debd94e6c4..22dce97100 100755 --- a/docs/reference/sql_reference/select.md +++ b/docs/reference/sql_reference/select.md @@ -26,7 +26,7 @@ sort_expressions: : expression [ASC|DESC][, expression [ASC|DESC], ...] ``` -For information on executing queries on Synopsis Data Engine, refer to [SDE](/../../aqp.md). +For information on executing queries on Approximate Query Processing, refer to [AQP](/../../aqp.md). ## Description Output data from one or more relations. diff --git a/docs/release_notes/TIB_compute-ce_1.2.0_relnotes.pdf b/docs/release_notes/TIB_compute-ce_1.2.0_relnotes.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b7898c353e301165ad6416018a9fe935999bf91a GIT binary patch literal 471227 zcmdSA1yo$iwl0jjYX|{4KyYc?9fAjUm&T!y#t9BVlHd-(U4j!_LLfLnf&_PW4fZ-@ z?{oIP=bU@r8~^y<7>~git5>f%t7gqv>zh?IzkZ=C`I4E9g$EPuMIH1H6O9c31b|I# zFwq1BS=BuqL97xc5EDDFC9ATDCCCN94t=c3s$k+^Ne6OZ)=;Adu&P0vUCkis&LEH~ z7z_chvGG01D4ICi$~stpq3!(o2r>sid%MGwI7OAWyO`@->P z2!NC8@k`FfCIMWJBLHx5K7Pr?{rmF!88`6pj{CO=_wQD?f2(u=a-xS zw% zkgBvev|V)*Qx~Yn?@9FASuOzJU;X;4;XnQQ(%KFJa%O#L2OYg6$P8=_VpRY+SVF7- 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\ No newline at end of file +[SnappyData 1.2.0 Release Notes](TIB_compute-ce_1.2.0_relnotes.pdf) diff --git a/docs/sde/hac_contracts.md b/docs/sde/hac_contracts.md index 4c89f80d2f..4687d301cf 100644 --- a/docs/sde/hac_contracts.md +++ b/docs/sde/hac_contracts.md @@ -2,15 +2,16 @@ This feature is available only in the Enterprise version of SnappyData.
-SnappyData combines state-of-the-art approximate query processing techniques and a variety of data synopses to ensure interactive analytics over both, streaming and stored data. Using high-level accuracy contracts (HAC), SnappyData offers end users intuitive means for expressing their accuracy requirements, without overwhelming them with statistical concepts. + +TIBCO ComputeDB combines state-of-the-art approximate query processing techniques and a variety of data synopses to ensure interactive analytics over both, streaming and stored data. Using high-level accuracy contracts (HAC), TIBCO ComputeDB offers end users intuitive means for expressing their accuracy requirements, without overwhelming them with statistical concepts. When an error constraint is not met, the action to be taken is defined in the behavior clause. ## Behavior Clause -Synopsis Data Engine has HAC support using the following behavior clause. +Approximate Query Processing (AQP) has HAC support using the following behavior clause. ### `` -The SDE engine returns the estimate as is. +The AQP engine returns the estimate as is. ![DO NOTHING](../Images/aqp_donothing.png) @@ -39,7 +40,7 @@ SELECT sum(ArrDelay) ArrivalDelay, Month_ from airline group by Month_ order by ``` ## Error Functions -In addition to this, SnappyData supports error functions that can be specified in the query projection. These error functions are supported for the SUM, AVG and COUNT aggregates in the projection. +In addition to this, TIBCO ComputeDB supports error functions that can be specified in the query projection. These error functions are supported for the SUM, AVG and COUNT aggregates in the projection. The following four methods are available to be used in query projection when running approximate queries: @@ -75,7 +76,7 @@ snc.table(basetable).groupBy("Year_").agg( avg("ArrDelay").alias("arrivalDelay), ``` ## Reserved Keywords -Keywords are predefined reserved words that have special meanings and cannot be used in a paragraph. Keyword `sample_` is reserved for SnappyData. +Keywords are predefined reserved words that have special meanings and cannot be used in a paragraph. Keyword `sample_` is reserved for TIBCO ComputeDB. If the aggregate function is aliased in the query as `sample_`, then what you get is true answers on the sample table, and not the estimates of the base table. diff --git a/docs/sde/key_concepts.md b/docs/sde/key_concepts.md index b5d4d1ac0e..ed72114aad 100644 --- a/docs/sde/key_concepts.md +++ b/docs/sde/key_concepts.md @@ -1,8 +1,8 @@ # Key Concepts -This feature is available only in the Enterprise version of SnappyData.
+This feature is available only in the Enterprise version of SnappyData.
-SnappyData SDE relies on two methods for approximations - **Stratified Sampling** and **Sketching**. A brief introduction to these concepts is provided below. +TIBCO ComputeDB AQP relies on two methods for approximations - **Stratified Sampling** and **Sketching**. A brief introduction to these concepts is provided below. ## Stratified Sampling Sampling is quite intuitive and commonly used by data scientists and explorers. The most common algorithm in use is 'uniform random sampling'. As the term implies, the algorithm is designed to randomly pick a small fraction of the population (the full data set). The algorithm is not biased on any characteristics in the data set. It is totally random and the probability of any element being selected in the sample is the same (or uniform). But, uniform random sampling does not work well for general purpose querying. @@ -25,9 +25,9 @@ Stratified sampling, on the other hand, allows the user to specify the common di To understand these concepts in further detail, refer to the [handbook](https://web.eecs.umich.edu/~mozafari/php/data/uploads/approx_chapter.pdf). It explains different sampling strategies, error estimation mechanisms, and various types of data synopses. ## Online Sampling -SDE also supports continuous sampling over streaming data and not just static data sets. For instance, you can use the Spark DataFrame APIs to create a uniform random sample over static RDDs. For online sampling, SDE first does [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling) for each startum in a write-optimized store before flushing it into a read-optimized store for stratified samples. -There is also explicit support for time series. For instance, if AdImpressions are continuously streaming in, SnappyData can ensure having enough samples over each 5-minute time window, while still ensuring that all GEOs have good representation in the sample. +AQP also supports continuous sampling over streaming data and not just static data sets. For instance, you can use the Spark DataFrame APIs to create a uniform random sample over static RDDs. For online sampling, AQP first does [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling) for each stratum in a write-optimized store before flushing it into a read-optimized store for stratified samples. +There is also explicit support for time series. For instance, if AdImpressions are continuously streaming in, TIBCO ComputeDB can ensure having enough samples over each 5-minute time window, while still ensuring that all GEOs have good representation in the sample. ## Sketching While stratified sampling ensures that data dimensions with low representation are captured, it still does not work well when you want to capture outliers. For instance, queries like 'Find the top-10 users with the most re-tweets in the last 5 minutes may not result in good answers. Instead, other data structures like a Count-min-sketch are relied on to capture data frequencies in a stream. This is a data structure that requires that it captures how often an element is seen in a stream for the top-N such elements. -While a [Count-min-sketch](https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch) is well described, SDE extends this with support for providing top-K estimates over time series data. +While a [Count-min-sketch](https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch) is well described, AQP extends this with support for providing top-K estimates over time series data. diff --git a/docs/sde/more_examples.md b/docs/sde/more_examples.md index a807e20b7c..f3b4328cb0 100644 --- a/docs/sde/more_examples.md +++ b/docs/sde/more_examples.md @@ -1,6 +1,6 @@ # More Examples -This feature is available only in the Enterprise version of SnappyData.
+This feature is available only in the Enterprise version of SnappyData.
## Example 1 Create a sample table with qcs 'medallion' @@ -46,7 +46,7 @@ snc.table(basetable).groupBy("hack_license").count().withError(.6,.90,"do_nothin ``` ## Example 3 -Create a sample table using function "hour(pickup_datetime) as QCS +Create a sample table using function "hour(pickup_datetime) as QCS: ```pre diff --git a/docs/sde/running_queries.md b/docs/sde/running_queries.md index 46851d085e..ad3ab2380b 100644 --- a/docs/sde/running_queries.md +++ b/docs/sde/running_queries.md @@ -1,14 +1,14 @@ # Running Queries -This feature is available only in the Enterprise version of SnappyData.
+This feature is available only in the Enterprise version of SnappyData.
Queries can be executed directly on sample tables or on the base table. Any query executed on the sample directly will always result in an approximate answer. When queries are executed on the base table users can specify their error tolerance and additional behavior to permit approximate answers. The Engine will automatically figure out if the query can be executed by any of the available samples. If not, the query can be executed on the base table based on the behavior clause. Here is the syntax: ```pre -> > SELECT ... FROM .. WHERE .. GROUP BY ...
-> > WITH ERROR ` `[CONFIDENCE` `] [BEHAVIOR `]` +SELECT ... FROM .. WHERE .. GROUP BY ...
+WITH ERROR ` `[CONFIDENCE` `] [BEHAVIOR `]` ``` * **WITH ERROR** - this is a mandatory clause. The values are 0 < value(double) < 1 . @@ -49,7 +49,7 @@ snc.table(baseTable).agg(Map("ArrDelay" -> "sum")).orderBy( desc("Month_")).with ``` ## Supporting BI Tools or Existing Apps -To allow BI tools and existing Apps that say might be generating SQL, SDE also supports specifying these options through your SQL connection or using the Snappy SQLContext. +To allow BI tools and existing Apps that say might be generating SQL, AQP also supports specifying these options through your SQL connection or using the Snappy SQLContext. ```pre snContext.sql(s"spark.sql.aqp.error=$error") @@ -58,17 +58,17 @@ snContext.sql(s"set spark.sql.aqp.behavior=$behavior") ``` These settings will apply to all queries executed via this SQLContext. Application can override this by also using the SQL extensions specified above. -Applications or tools using JDBC/ODBC can set the following properties: - +Applications or tools using JDBC/ODBC can set the following properties. + * When using Apache Zeppelin JDBC interpreter or the Snappy SQL you can set the values as follows: - ```pre - set spark.sql.aqp.error=$error; - set spark.sql.aqp.confidence=$confidence; - set spark.sql.aqp.behavior=$behavior; - ``` -* Setting AQP specific properties as a connection level property using JDBC: + set spark.sql.aqp.error=$error; + set spark.sql.aqp.confidence=$confidence; + set spark.sql.aqp.behavior=$behavior; + +* Setting AQP specific properties as a connection level property using JDBC: + Properties prop = new Properties(); prop.setProperty("spark.sql.aqp.error","0.3"); prop.setProperty("spark.sql.aqp.behavior","local_omit"); diff --git a/docs/sde/sample_selection.md b/docs/sde/sample_selection.md index de8d47d8f8..3bc57239a4 100644 --- a/docs/sde/sample_selection.md +++ b/docs/sde/sample_selection.md @@ -1,6 +1,6 @@ # Sample Selection -This feature is available only in the Enterprise version of SnappyData.
+This feature is available only in the Enterprise version of SnappyData.
Sample selection logic selects most appropriate sample, based on this relatively simple logic in the current version: diff --git a/docs/sde/sketching.md b/docs/sde/sketching.md index 9d996aa22e..1965406d8b 100644 --- a/docs/sde/sketching.md +++ b/docs/sde/sketching.md @@ -1,13 +1,13 @@ # Sketching -This feature is available only in the Enterprise version of SnappyData.
+This feature is available only in the Enterprise version of SnappyData.
Synopses data structures are typically much smaller than the base data sets that they represent. They use very little space and provide fast, approximate answers to queries. A [BloomFilter](https://en.wikipedia.org/wiki/Bloom_filter) is a commonly used example of a synopsis data structure. Another example of a synopsis structure is a [Count-Min-Sketch](https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch) which serves as a frequency table of events in a stream of data. The ability to use Time as a dimension for querying makes synopses structures much more useful. As streams are ingested, all relevant synopses are updated incrementally and can be queried using SQL or the Scala API. ## Creating TopK tables TopK queries are used to rank attributes to answer "best, most interesting, most important" class of questions. TopK structures store elements ranking them based on their relevance to the query. [TopK](http://stevehanov.ca/blog/index.php?id=122) queries aim to retrieve, from a potentially very large result set, only the *k (k >= 1)* best answers. -### SQL API for creating a TopK table in SnappyData +### SQL API for creating a TopK table in TIBCO ComputeDB ```pre snsc.sql("create topK table MostPopularTweets on tweetStreamTable " + @@ -28,7 +28,7 @@ The example above create a TopK table called MostPopularTweets, the base table f snc.createApproxTSTopK("MostPopularTweets", Some("tweetStreamTable"), "HashTag", schema, topKOptionMap) -The code above shows how to do the same thing using the SnappyData Scala API. +The code above shows how to do the same thing using the TIBCO ComputeDB Scala API. ### Querying the TopK table @@ -40,7 +40,7 @@ The example above queries the TopK table which returns the top 40 (the depth of ## Approximate TopK analytics for time series data Time is used as an attribute in creating the TopK structures. Time can be an attribute of the incoming data set (which is frequently the case with streaming data sets) and in the absence of that, the system uses arrival time of the batch as the time stamp for that incoming batch. The TopK structure is populated along the dimension of time. As an example, the most re-tweeted hashtags in each window are stored in the data structure. This allows us to issue queries like, "what are the most popular hashtags in a given time interval?" Queries of this nature are typically difficult to execute and not easy to optimize (due to space considerations) in a traditional system. -Here is an example of a time-based query on the TopK structure which returns the most popular hashtags in the time interval queried. The SnappyData SDE module provides two attributes startTime and endTime which can be used to run queries on arbitrary time intervals. +Here is an example of a time-based query on the TopK structure which returns the most popular hashtags in the time interval queried. The TIBCO ComputeDB AQP module provides two attributes startTime and endTime which can be used to run queries on arbitrary time intervals. select hashtag, EstimatedValue, ErrorBoundsInfo from MostPopularTweets where @@ -50,7 +50,7 @@ Here is an example of a time-based query on the TopK structure which returns the If time is an attribute in the incoming data set, it can be used instead of the system generated time. In order to do this, the TopK table creation is provided the name of the column containing the timestamp. -### SQL API for creating a TopK table in SnappyData specifying timestampColumn +### SQL API for creating a TopK table in TIBCO ComputeDB specifying timestampColumn In the example below tweetTime is a field in the incoming dataset which carries the timestamp of the tweet. @@ -75,6 +75,6 @@ The example above create a TopK table called MostPopularTweets, the base table f "HashTag", schema, topKOptionMap) ``` -The code above shows how to do the same thing using the SnappyData Scala API. +The code above shows how to do the same thing using the TIBCO ComputeDB Scala API. It is worth noting that the user has the ability to disable time as a dimension if desired. This is done by not providing the *timeInterval* attribute when creating the TopK table. diff --git a/docs/sde/working_with_stratified_samples.md b/docs/sde/working_with_stratified_samples.md index cf3cb9464f..079ab8c1b7 100644 --- a/docs/sde/working_with_stratified_samples.md +++ b/docs/sde/working_with_stratified_samples.md @@ -1,17 +1,18 @@ # Working with Stratified Samples -This feature is available only in the Enterprise version of SnappyData.
+This feature is available only in the Enterprise version of SnappyData.
## Create Sample Tables You can create sample tables on datasets that can be sourced from any source supported in Spark/TIBCO ComputeDB. For instance, these can be TIBCO ComputeDB in-memory tables, Spark DataFrames, or sourced from an external data source such as S3 or HDFS. Creation of sample table will implicitly sample the data from the base table. -Here is an SQL based example to create a sample on tables locally available in the SnappyData cluster. +Here is an SQL based example to create a sample on tables locally available in the TIBCO ComputeDB cluster. ```pre CREATE SAMPLE TABLE NYCTAXI_PICKUP_SAMPLE ON NYCTAXI - OPTIONS (qcs 'hour(pickup_datetime)', fraction '0.01'); + OPTIONS (qcs 'hour(pickup_datetime)', fraction '0.01'); + CREATE SAMPLE TABLE TAXIFARE_HACK_LICENSE_SAMPLE on TAXIFARE OPTIONS (qcs 'hack_license', fraction '0.01'); @@ -22,13 +23,13 @@ In this example below, a sample table is created for an S3 (external) dataset: ```pre CREATE EXTERNAL TABLE TAXIFARE USING parquet - OPTIONS(path 's3a://:@zeppelindemo/nyctaxifaredata_cleaned'); + OPTIONS(path 's3a://:@computedb-test-data/nyctaxifaredata_cleaned'); //Next, create the sample sourced from this table .. CREATE SAMPLE TABLE TAXIFARE_HACK_LICENSE_SAMPLE on TAXIFARE options (qcs 'hack_license', fraction '0.01'); ``` -When creating a base table, if you have applied the **partition by** clause, the clause is also applied to the sample table. The sample table also inherits the **number of buckets**, **redundancy** and **persistence** properties from the base table. +When creating a base table, if you have applied the **partition by** clause, the clause is also applied to the sample table. For sample tables, the **overflow** property is set to **False** by default. (For row and column tables the default value is **True**). @@ -41,23 +42,25 @@ USING COLUMN OPTIONS (partition_by '', Buckets '7', Redundancy '1 CREATE TABLE SAMPLETABLENAME USING COLUMN_SAMPLE OPTIONS (qcs '',fraction '0.05', strataReservoirSize '50', baseTable 'baseTableName') -// In this case, sample table 'sampleTableName' is partitioned by column 'column_name_a', has 7 buckets and 1 redundancy. + ``` + !!! Note - * After a sample table is created from a base table, any changes to the base table, (for example update and delete operations) is not automatically applied to the sample table. + * After a sample table is created from a base table, any changes to the base table, (for example update and delete operations) is not automatically applied to the sample table. * Howerver, if the data is populated in the main table using any of the following ways: * dataFrame.write * Using JDBC API batch insert - * Insert into table values select * from x
Then the data is also sampled and put into sample table(s) if exists. - * For successful creation of sample tables, the number of buckets in the sample table should be more than the number of nodes in the cluster. + * Insert into table values select * from x + Then the data is also sampled and put into sample table(s) if exists. + * For successful creation of sample tables, the number of buckets in the sample table should be more than the number of nodes in the cluster. ## QCS (Query Column Set) and Sample Selection For stratified samples, you are required to specify the columns used for stratification(QCS) and how big the sample needs to be (fraction). QCS, which stands for Query Column Set is typically the most commonly used dimensions in your query GroupBy/Where and Having clauses. A QCS can also be constructed using SQL expressions - for instance, using a function like `hour (pickup_datetime)`. -The parameter *fraction* represents the fraction of the full population that is managed in the sample. Intuition tells us that higher the fraction, more accurate the answers. But, interestingly, with large data volumes, you can get pretty accurate answers with a very small fraction. With most data sets that follow a normal distribution, the error rate for aggregations exponentially drops with the fraction. So, at some point, doubling the fraction does not drop the error rate. SDE always attempts to adjust its sampling rate for each stratum so that there is enough representation for all sub-groups. +The parameter *fraction* represents the fraction of the full population that is managed in the sample. Intuition tells us that higher the fraction, more accurate the answers. But, interestingly, with large data volumes, you can get pretty accurate answers with a very small fraction. With most data sets that follow a normal distribution, the error rate for aggregations exponentially drops with the fraction. So, at some point, doubling the fraction does not drop the error rate. AQP always attempts to adjust its sampling rate for each stratum so that there is enough representation for all sub-groups. For instance, in the above example, taxi drivers that have very few records may actually be sampled at a rate much higher than 1% while very active drivers (a lot of records) is automatically sampled at a lower rate. The algorithm always attempts to maintain the overall 1% fraction specified in the 'create sample' statement. One can create multiple sample tables using different sample QCS and sample fraction for a given base table. diff --git a/docs/security/authorization.md b/docs/security/authorization.md index 1c47f94c57..f015bede7f 100644 --- a/docs/security/authorization.md +++ b/docs/security/authorization.md @@ -2,7 +2,7 @@ This feature is available only in the Enterprise version of SnappyData.
-Authorization is the process of determining what access permissions the authenticated user has. Users are authorized to perform tasks based on their role assignments. SnappyData also supports LDAP group authorization. +Authorization is the process of determining what access permissions the authenticated user has. Users are authorized to perform tasks based on their role assignments. TIBCO ComputeDB also supports LDAP group authorization. The administrator can manage user permissions in a secure cluster using the [GRANT](../reference/sql_reference/grant.md) and [REVOKE](../reference/sql_reference/revoke.md) SQL statements which allow you to set permission for the user for specific database objects or for specific SQL actions. @@ -16,7 +16,7 @@ The [GRANT](../reference/sql_reference/grant.md) statement is used to grant spec ### Adding Restrictions in Default Schema -Users in SnappyData cluster have their own schema by default when they log into the cluster. They have full access within this schema. +Users in TIBCO ComputeDB cluster have their own schema by default when they log into the cluster. They have full access within this schema. But in some cases, cluster administrators may need to ensure controlled use of the cluster resources by its users and may need to enforce restrictions on them. This can be achieved by setting the system property `snappydata.RESTRICT_TABLE_CREATION` to true in **conf** files at the time of starting the cluster. @@ -33,14 +33,21 @@ localhost -auth-provider=LDAP -J-Dsnappydata.RESTRICT_TABLE_CREATION=true -user= -J-Dgemfirexd.auth-ldap-search-pw=user123 ``` -## LDAP Groups in SnappyData Authorization -SnappyData extends the SQL GRANT statement to support LDAP Group names as Grantees. +## LDAP Groups in TIBCO ComputeDB Authorization +TIBCO ComputeDB extends the SQL GRANT statement to support LDAP Group names as Grantees. + +Here is an example SQL to grant privileges to individual users on column/row tables: -Here is an example SQL to grant privileges to individual users: ```pre GRANT SELECT ON TABLE t TO sam,bob; ``` +Here is an example SQL to grant privileges to individual users on external tables: + +```pre +GRANT ALL ON EXT_T1 TO samb,bob; +``` + You can also grant privileges to LDAP groups using the following syntax: ```pre @@ -48,8 +55,14 @@ GRANT SELECT ON Table t TO ldapGroup:, bob; GRANT INSERT ON Table t TO ldapGroup:, bob; ``` -SnappyData fetches the current list of members for the LDAP Group and grants each member privileges individually (stored in SnappyData).
-Similarly, when a REVOKE SQL statement is executed SnappyData removes the privileges individually for all members that make up a group. To support changes to Group membership within the LDAP Server, there is an additional System procedure to refresh the privileges recorded in SnappyData. +You can also grant privileges to LDAP groups using the following syntax on external tables: + +```pre +GRANT ALL ON EXT_T1 TO ldapGroup:, bob; +``` + +TIBCO ComputeDB fetches the current list of members for the LDAP Group and grants each member privileges individually (stored in TIBCO ComputeDB).
+Similarly, when a REVOKE SQL statement is executed TIBCO ComputeDB removes the privileges individually for all members that make up a group. To support changes to Group membership within the LDAP Server, there is an additional System procedure to refresh the privileges recorded in TIBCO ComputeDB. ```pre CALL SYS.REFRESH_LDAP_GROUP(''); @@ -69,7 +82,7 @@ gemfirexd.group-ldap-member-attributes ``` If no `gemfirexd.group-ldap-search-base` property has been provided then the one used for authentication `gemfirexd.auth-ldap-search-base` is used.
-If no search filter is specified then SnappyData uses the standard objectClass groupOfMembers (rfc2307) or groupOfNames with attribute as member, or objectClass groupOfUniqueMembers with attribute as uniqueMember. +If no search filter is specified then TIBCO ComputeDB uses the standard objectClass groupOfMembers (rfc2307) or groupOfNames with attribute as member, or objectClass groupOfUniqueMembers with attribute as uniqueMember. To be precise, the default search filter is: ```pre @@ -98,3 +111,4 @@ member: cn=group11,ou=group,dc=example,dc=com * If a user belongs to LDAP group as well as granted permissions separately as a user, then the latter is given precedence. So even if LDAP group permission is later revoked (or user is removed from LDAP group), the user will continue to have permissions unless explicitly revoked as a user. * LDAPGROUP is now a reserved word, so cannot be used for a user name. + diff --git a/docs/setting_up_odbc_driver-tableau_desktop.md b/docs/setting_up_odbc_driver-tableau_desktop.md index 45f897ccac..bd0b474da5 100644 --- a/docs/setting_up_odbc_driver-tableau_desktop.md +++ b/docs/setting_up_odbc_driver-tableau_desktop.md @@ -61,3 +61,4 @@ The following instructions describe how to configure SSL in a DSN: * Enter the **ciphers** that you want to use. This is an optional input. If left empty then default ciphers are `"ALL:!ADH:!LOW:!EXP:!MD5:@STRENGTH"` For information about connecting Tableau using TIBCO ComputeDB ODBC Driver, refer to [Connect Tableau using ODBC Driver](./howto/tableauconnect.md#odbcdritab) + diff --git a/docs/troubleshooting/collect_debug_artifacts.md b/docs/troubleshooting/collect_debug_artifacts.md index 74979732c9..ebb135e830 100644 --- a/docs/troubleshooting/collect_debug_artifacts.md +++ b/docs/troubleshooting/collect_debug_artifacts.md @@ -1,4 +1,4 @@ -# Collecting logs, stats and dumps using the collect-debug-artifacts script +# Collecting Logs, Stats, and Dumps using the collect-debug-artifacts script This section uses the term 'node' frequently. A node denotes a server or a locator member when a purely SnappyData system is there. In a SnappyData distributed system a node can mean server, locator or lead member. @@ -66,3 +66,5 @@ Options: Timestamp format: YYYY-MM-DD HH:MM[:SS] + +For offline analysis, as the production logs can be pretty huge and not easily sharable, you can provide a list of patterns which can be excluded for collection. A list of patterns can be specified in the **conf** > **debug.conf** file. Before doing this, you must rename the **debug.conf.template** file to **debug.conf**. The lines matching those patterns will be ignored by the script and excluded for collection. \ No newline at end of file diff --git a/docs/unsupported.md b/docs/unsupported.md new file mode 100644 index 0000000000..89e827491f --- /dev/null +++ b/docs/unsupported.md @@ -0,0 +1,10 @@ +# Unsupported Third-Party Modules +The following third-party modules are not supported by SnappyData 1.2.0 although it is shipped with the product: + +* Spark RDD-based APIs: + * **org.apache.spark.mllib** + * **GraphX (Graph Processing)** + * **Spark Streaming (DStreams)** + +• SnappyData does not support **SparkR (R on Spark)** or **Apache Zeppelin**. + diff --git a/mkdocs.yml b/mkdocs.yml index bbf8898f04..724f366531 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -74,11 +74,11 @@ pages: - 'How to Connect using JDBC Driver': 'howto/connect_using_jdbc_driver.md' - 'How to use SnappyData for any Spark Distribution': 'howto/using_snappydata_for_any_spark_dist.md' - 'How to Store and Retrieve Complex Data Types in JDBC Programs': 'howto/store_retrieve_complex_datatypes_JDBC.md' - - 'How to Store and Query JSON Objects': 'howto/store_and_query_json_objects.md' +# - 'How to Store and Query JSON Objects': 'howto/store_and_query_json_objects.md' - 'How to Store and Query Objects': 'howto/store_and_query_objects.md' - 'How to use Stream Processing with SnappyData': 'howto/use_stream_processing_with_snappydata.md' - 'How to use Transactions Isolation Levels': 'howto/use_transactions_isolation_levels.md' - - 'How to use Synopsis Data Engine to Run Approximate Queries': 'howto/use_synopsis_data_engine_to_run_approximate_queries.md' + - 'How to use Approximate Query Processing (AQP) to Run Approximate Queries': 'howto/use_synopsis_data_engine_to_run_approximate_queries.md' - 'How to use Python to Create Tables and Run Queries': 'howto/use_python_to_create_tables_and_run_queries.md' - 'How to Connect using ODBC Driver': 'howto/connect_using_odbc_driver.md' - 'How to Connect to the Cluster from External Network': 'howto/connect_to_the_cluster_from_external_clients.md' @@ -97,7 +97,7 @@ pages: - 'Using Snappy Shell': 'programming_guide/using_snappydata_shell.md' - 'Using the Spark Shell and spark-submit': 'programming_guide/using_the_spark_shell_and_spark-submit.md' - 'Using pyspark Shell': 'programming_guide/using_pyspark_shell.md' - - 'Using the Scala Interpreter': 'programming_guide/scala_interpreter.md' + - 'Executing Spark Scala Code using SQL': 'programming_guide/scala_interpreter.md' - 'Working with Hadoop YARN cluster Manager': 'programming_guide/working_with_hadoop_yarn_cluster_manager.md' - 'Using JDBC with SnappyData': 'programming_guide/using_jdbc_with_snappydata.md' - 'Accessing SnappyData Tables from any Spark (2.1+) Cluster': 'programming_guide/spark_jdbc_connector.md' @@ -132,9 +132,9 @@ pages: - 'Supported Data Formats': 'Data/data_formats.md' - 'Accessing Cloud Storages': 'connectors/access_cloud_data.md' - 'Connecting to External Hive Metastores': 'Data/external_hive_support.md' - - 'Deploying Third Party Connectors': 'connectors/deployment_dependency_jar.md' - 'Using the SnappyData GemFire Connector': 'connectors/gemfire_connector.md' - 'Using the SnappyData CDC (Change Data Capture) Connector': 'connectors/cdc_connector.md' + - 'Deploying Third Party Connectors': 'connectors/deployment_dependency_jar.md' - 'Managing and Monitoring': 'monitoring/managing_and_monitoring.md' - 'Managing and Monitoring': - 'SnappyData Monitoring UI': 'monitoring/monitoring.md' @@ -201,14 +201,17 @@ pages: - 'DEPLOY PACKAGE': 'reference/sql_reference/deploy_package.md' - 'DEPLOY JAR': 'reference/sql_reference/deploy_jar.md' - 'UNDEPLOY': 'reference/sql_reference/undeploy.md' + - 'exec scala': 'reference/sql_reference/exec-scala.md' - 'EXPLAIN': 'reference/sql_reference/explain.md' - 'GRANT': 'reference/sql_reference/grant.md' + - 'GRANT ALL': 'reference/sql_reference/grant-all.md' - 'INSERT': 'reference/sql_reference/insert.md' - 'LATERAL VIEW': 'reference/sql_reference/lateral-view.md' - 'LIST': 'reference/sql_reference/list.md' - 'PIVOT': 'reference/sql_reference/pivot.md' - 'PUT INTO': 'reference/sql_reference/put-into.md' - 'REVOKE': 'reference/sql_reference/revoke.md' + - 'REVOKE ALL': 'reference/sql_reference/revoke_all.md' - 'SELECT': 'reference/sql_reference/select.md' - 'SET ISOLATION': 'reference/sql_reference/set-isolation.md' - 'SET SCHEMA': 'reference/sql_reference/set-schema.md' @@ -241,6 +244,7 @@ pages: - 'revoke-missing-disk-store': 'reference/command_line_utilities/store-revoke-missing-disk-stores.md' - 'unblock-disk-store': 'reference/command_line_utilities/store-unblock-disk-stores.md' - 'run': 'reference/command_line_utilities/store-run.md' + - 'scala CLI': 'reference/command_line_utilities/scala-cli.md' - 'version': 'reference/command_line_utilities/store-version.md' - 'Snappy-SQL Shell Interactive Commands': 'reference/interactive_commands/store_command_reference.md' - 'Snappy-SQL Shell Interactive Commands': @@ -320,6 +324,7 @@ pages: - 'Configuring SSH Login without Password': 'reference/misc/passwordless_ssh.md' - 'Setting Up SnappyData ODBC Driver': 'setting_up_odbc_driver-tableau_desktop.md' - 'Setting Up SnappyData JDBC Client and QlikView': 'setting_up_jdbc_driver_qlikview.md' +- 'Experimental Features': 'experimental.md' - 'Troubleshooting Common Problems': 'troubleshooting/troubleshooting.md' - 'Troubleshooting Common Problems': - 'Member Startup Problems': 'troubleshooting/member_startup_problems.md' @@ -330,6 +335,7 @@ pages: - 'Troubleshooting Out-of-Memory (OOM) Error': 'troubleshooting/oom.md' - 'Collecting Logs, Stats and Dumps Using the collect-debug-artifacts Script': 'troubleshooting/collect_debug_artifacts.md' - 'Troubleshooting Error Messages': 'troubleshooting/troubleshooting_error_messages.md' +- 'Unsupported Third-Party Modules': 'unsupported.md' #- Additional Information: additional_files/additional_docs.md #- About: # - Contributing: about/contributing.md diff --git a/store b/store index d0f2973663..fceceb8571 160000 --- a/store +++ b/store @@ -1 +1 @@ -Subproject commit d0f297366377422145e53b6dadd8112242a58ed0 +Subproject commit fceceb8571f6668d82275b5cee7380c84900ca7f